并联模糊推理系统补偿器在球形电机控制中的改进设计(IJISA-V6-N3-2)
I.J. Intelligent Systems and Applications, 2014, 03, 12-25
Published Online February 2014 in MECS (https://www.wendangku.net/doc/0115579460.html,/)
DOI: 10.5815/ijisa.2014.03.02
Design Modified Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Spherical Motor
Alireza Siahbazi, Ali Barzegar, Mahmood Vosoogh, Abdol Majid Mirshekaran, Samira Soltani Research and Development Department, Institute of Advance Science and Technology-IRAN SSP, Shiraz, Iran https://www.wendangku.net/doc/0115579460.html,; E-mail: SSP.ROBOTIC@https://www.wendangku.net/doc/0115579460.html,
Abstract— The increasing demand for multi-degree-of-freedom (DOF) actuators in a number of industries has motivated a flurry of research in the development of non-conventional actuators, spherical motor. This motor is capable of providing smooth and isotropic three-dimensional motion in a single joint. Not only can the spherical motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor, however, exhibits coupled, nonlinear and very complex dynamics that make the design and implementation of feedback controllers very challenging. The orientation-varying torque generated by the spherical motor also contributes to the challenges in controller design.This paper contributes to the on-going research effort by exploring alternate methods for nonlinear and robust controlling the motor.The robust sliding mode controller proposed in this paper is used to further demonstrate the appealing features exhibited by the spherical motor.In opposition, sliding mode controller is used in many applications especially to control of highly uncertain systems; it has two significant drawbacks namely; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system (e.g., spherical motor) can be solved by using artificial intelligence theorem and applied a modified linear controller to switching part of sliding mode controller. Using Lyapunov-type stability arguments, a robust modified linear fuzzy sliding mode controller is designed to achieve this objective. The controller developed in this paper is designed in a robust stabilizing torque is designed for the nominal spherical motor dynamics derived using the constrained Lagrangian formulation. The eventual stability of the controller depends on the torque generating capabilities of the spherical motor.
Index Terms—Fuzzy Sliding Mode Algorithm, Spherical Motor, Lyapunov Based, Chattering Phenomenon, Fuzzy Logic Controller I.Introduction
Multi-degree-of-freedom (DOF) actuators are finding wide use in a number of Industries. Currently, a significant number of the existing robotic actuators that can realize multi-DOF motion are constructed using gear and linkages to connect several single-DOF motors in series and/or parallel. Not only do such actuators tend to be large in size and mass, but they also have a decreased positioning accuracy due to mechanical deformation, friction and backlash of the gears and linkages. A number of these systems also exhibit singularities in their workspaces, which makes it virtually impossible to obtain uniform, high-speed, and high-precision motion. For high precession trajectory planning and control, it is necessary to replace the actuator system made up of several single-DOF motors connected in series and/or parallel with a single multi-DOF actuator. The need for such systems has motivated years of research in the development of unusual, yet high performance actuators that have the potential to realize multi-DOF motion in a single joint. One such actuator is the spherical motor. Compared to conventional robotic manipulators that offer the same motion capabilities, the spherical motor possesses several advantages. Not only can the motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor is much simpler and more compact in design than most multiple single-axis robotic manipulators. The motor is also relatively easy to manufacture. The spherical motor have potential contributions to a wide range of applications such as coordinate measuring, object tracking, material handling, automated assembling, welding, and laser cutting. All these applications require high precision motion and fast dynamic response, which the spherical motor is capable of delivering. Previous research efforts on the spherical motor have demonstrated most of these features. These, however, come with a number of challenges. The spherical motor exhibits coupled, nonlinear and very complex dynamics. The design and implementation of feedback controllers for the motor are complicated by these dynamics. The controller design is further complicated by the orientation-varying torque generated by the spherical motor. Some of these
challenges have been the focus of previous and ongoing
research [1-11].
In modern usage, the word of control has many meanings, this word is usually taken to mean regulate,
direct or command. The word feedback plays a vital role in the advance engineering and science. The conceptual frame work in Feed-back theory has
developed only since world war ??. In the twentieth century, there was a rapid growth in the application of feedback controllers in process industries. According to
Ogata, to do the first significant work in three-term or PID controllers which Nicholas Minorsky worked on it by automatic controllers in 1922. In 1934, Stefen Black
was invention of the feedback amplifiers to develop the negative feedback amplifier[12-28]. Negative feedback invited communications engineer Harold Black in 1928
and it occurs when the output is subtracted from the input. Automatic control has played an important role in advance science and engineering and its extreme
importance in many industrial applications, i.e., aerospace, mechanical engineering and joint control. The first significant work in automatic control was James Watt’s centrifugal governor for the speed control in motor engine in eighteenth century[29-40]. There are several methods for controlling a spherical motor,
which all of them follow two common goals, namely, hardware/software implementation and acceptable performance. However, the mechanical design of
spherical motor is very important to select the best controller but in general two types schemes can be presented, namely, a joint space control schemes and an
operation space control schemes[41-53]. Joint space and operational space control are closed loop controllers which they have been used to provide robustness and
rejection of disturbance effect. The main target in joint space controller is to design a feedback controller which the actual motion ( () ) and desired motion ( ( ) ) as closely as possible. This control problem is classified into two main groups. Firstly, transformation the
desired motion ( ) to joint variable ( ) by inverse kinematics of spherical motor[34-50]. This control
includes simple PD control, PID control, inverse dynamic control, Lyapunov-based control, and passivity based control. The main target in operational space
controller is to design a feedback controller to allow the actual end-effector motion ( )to track the desired endeffector motion( ). This control methodology requires a greater algorithmic complexity and the
inverse kinematics used in the feedback control loop. Direct measurement of operational space variables are very expensive that caused to limitation used of this controller in spherical motor[50-53]. One of the simplest ways to analysis control of three DOF spherical motor are analyzed each joint separately such as SISO systems and design an independent joint controller for each joint. In this controller, inputs only depends on the velocity and displacement of the corresponding joint and the other parameters between joints such as coupling presented by disturbance input. Joint space controller has many advantages such as one type controllers design for all joints with the same formulation, low cost hardware, and simple structure. A nonlinear methodology is used for nonlinear uncertain systems (e.g., spherical motor) to have an acceptable performance. These controllers divided into six groups, namely, feedback linearization (computed-torque control), passivity-based control, sliding mode control (variable structure control), artificial intelligence control, lyapunov-based control and adaptive control[13-26]. Sliding mode controller (SMC) is a powerful nonlinear controller which has been analyzed by many researchers especially in recent years. This theory was first proposed in the early 1950 by Emelyanov and several co-workers and has been extensively developed since then with the invention of high speed control devices [12-18]. The main reason to opt for this controller is its acceptable control performance in wide range and solves two most important challenging topics in control which names, stability and robustness [24-53]. Sliding mode controller is divided into two main sub controllers: discontinues controller( ) and equivalent controller( ). Discontinues controller causes an acceptable tracking performance at the expense of very fast switching. In the theory of infinity fast switching can provide a good tracking performance but it also can provide some problems(e.g., system instability and chattering phenomenon). After going toward the sliding surface by discontinues term, equivalent term help to the system dynamics match to the sliding surface[12-15]. However, this controller used in many applications but, pure sliding mode controller has following challenges: chattering phenomenon, and nonlinear equivalent dynamic formulation [20]. Chattering phenomenon can causes some problems such as saturation and heat the mechanical parts of spherical motor. To reduce or eliminate the chattering, various papers have been reported by many researchers which classified into two most important methods: boundary layer saturation method and estimated uncertainties method [22-36]. In boundary layer saturation method, the basic idea is the discontinuous method replacement by saturation (linear) method with small neighborhood of the switching surface. This replacement caused to increase the error performance against with the considerable chattering reduction. In recent years, artificial intelligence theory has been used in sliding mode control systems. Neural network, fuzzy logic and neuro-fuzzy are synergically combined with nonlinear classical controller and used in nonlinear, time variant and uncertain plant (e.g., spherical motor). Fuzzy logic controller (FLC) is one of the most important applications of fuzzy logic theory. This controller can be used to control nonlinear, uncertain, and noisy systems. This method is free of some model techniques as in model-based controllers. As mentioned that fuzzy logic application is not only limited to the modelling of nonlinear systems [31-36] but also this method can help engineers to design a model-free controller. Control spherical motor using model-based controllers are based
on manipulator dynamic model. These controllers often have many problems for modelling. Conventional controllers require accurate information of dynamic model of spherical motor, but most of time these models are MIMO, nonlinear and partly uncertain therefore calculate accurate dynamic model is complicated [32]. The main reasons to use fuzzy logic methodology are able to give approximate recommended solution for uncertain and also certain complicated systems to easy understanding and flexible. Fuzzy logic provides a method to design a model-free controller for nonlinear plant with a set of IF-THEN rules [32]. This paper contributes to the research effort of alternate methods for modeling the torque generated by the spherical motor used in the fuzzy sliding mode-type feedback controller design. The designed controller not only demonstrates the appealing features exhibited by the spherical motor, but also demonstrates some of the nice features of fuzzy sliding mode-type controllers as well. This paper is organized as follows; second part focuses on the modeling dynamic formulation based on Lagrange methodology, sliding mode controller to have a robust control, and design fuzzy logic compensator. Third part is focused on the methodology which can be used to reduce the error, increase the performance quality and increase the robustness and stability. Simulation result and discussion is illustrated in forth part which based on trajectory following and disturbance rejection. The last part focuses on the conclusion and compare between this method and the other ones.
II.Theorem
Dynamic and Kinematics Formulation of Spherical Motor
Dynamic modeling of spherical motors is used to describe the behavior of spherical motor such as linear or nonlinear dynamic behavior, design of model based controller such as pure sliding mode controller which design this controller is based on nonlinear dynamic equations, and for simulation. The dynamic modeling describes the relationship between motion, velocity, and accelerations to force/torque or current/voltage and also it can be used to describe the particular dynamic effects (e.g., inertia, coriolios, centrifugal, and the other parameters) to behavior of system[1-10]. Spherical motor has a nonlinear and uncertain dynamic parameters 3 degrees of freedom (DOF) motor.
The equation of a spherical motor governed by the following equation [1-10]:
( )[] ( )[] ( )[][]
(1)
Where τ is actuation torque, H (q) is a symmetric and positive define inertia matrix, B(q) is the matrix of coriolios torques, C(q) is the matrix of centrifugal torques.
This is a decoupled system with simple second order linear differential dynamics. In other words, the component ? influences, with a double integrator relationship, only the variable, independently of the motion of the other parts. Therefore, the angular acceleration is found as to be [1-11]:
()** ++(2) This technique is very attractive from a control point of view.
Study of spherical motor is classified into two main groups: kinematics and dynamics. Calculate the relationship between rigid bodies and final part without any forces is called Kinematics. Study of this part is pivotal to design with an acceptable performance controller, and in real situations and practical applications. As expected the study of kinematics is divided into two main parts: forward and inverse kinematics. Forward kinematics has been used to find the position and orientation of task frame when angles of joints are known. Inverse kinematics has been used to find possible joints variable (angles) when all position and orientation of task frame be active [1].
The main target in forward kinematics is calculating the following function:
()(3) Where ( ) is a nonlinear vector function, , - is the vector of task space variables which generally task frame has three task space variables, three orientation, , -is a vector of angles or displacement, and finally is the number of actuated joints. The Denavit-Hartenberg (D-H) convention is a method of drawing spherical motor free body diagrams. Denvit-Hartenberg (D-H) convention study is necessary to calculate forward kinematics in this motor.
A systematic Forward Kinematics solution is the main target of this part. The first step to compute Forward Kinematics (F.K) is finding the standard D-H parameters. The following steps show the systematic derivation of the standard D-H parameters.
1.Locate the spherical motor
https://www.wendangku.net/doc/0115579460.html,bel joints
3.Determine joint rotation ( )
4.Setup base coordinate frames.
5.Setup joints coordinate frames.
6.Determine, that, link twist, is the angle between
and about an .
7.Determine and , that , link length, is the distance between and along . , offset, is the distance between and along axis.
8.Fill up the D-H parameters table. The second step to compute Forward kinematics is finding the rotation matrix (). The rotation matrix from*+ to *+ is given by the following equation;
() ( )(4) Where () is given by the following equation [1-11];
()[
() ( )
( )()]
(5)
and ( ) is given by the following equation [1-11];
()[() ( )
( )()
]
(6)
So () is given by [8]
()()()(7) The final step to compute the forward kinematics is calculate the transformation by the following formulation [3]
[](8) SLIDING MODE CONTROLLER: One of the significant challenges in control algorithms is a linear behavior controller design for nonlinear systems. When system works with various parameters and hard nonlinearities this technique is very useful in order to be implemented easily but it has some limitations such as working near the system operating point[12]. Some of nonlinear systems which work in industrial processes are controlled by linear PID controllers, but the design of linear controller for spherical motors are extremely difficult because they are nonlinear, uncertain and MIMO[33-55]. To reduce above challenges the nonlinear robust controllers is used to systems control. One of the powerful nonlinear robust controllers is sliding mode controller (SMC), although this controller has been analyzed by many researchers but the first proposed was in the 1950 [12-33].This controller is used in wide range areas such as in robotics, in control process, in aerospace applications and in power converters because it has an acceptable control performance and solve some main challenging topics in control such as resi stivity to the external disturbance. The lyapunov formulation can be written as follows,
(9)
The derivation of can be determined as,
(10) The dynamic equation of spherical motor can be written based on the sliding surface as
(11) It is assumed that
()(12)
by substituting (11) in (10)
()
()
(13) Suppose the control input is written as follows
???[?(?)]?
()
(14) By replacing the equation (14) in (13)
( ??
().?
()/
(15)
It is obvious that
|??||?||?|||(16)
The Lemma equation in spherical motor system can be written as follows
[|?|||||](17) The equation (12) can be written as
|[]|(18)
Therefore, it can be shown that
∑||
(19)
Based on above discussion, the control law for spherical motor is written as:
(20) Where, the model-based component is the nominal dynamics of systems and can be calculate as follows:
[()](21)
is computed as;
()(22) by replace the formulation (22) in (20) the control output can be written as;
()(23) By (23) and (21) the sliding mode control of spherical motor is calculated as;
[()]()(24) FUZZY LOGIC THEORY: This section provides a review about foundation of fuzzy logic based on [32- 53]. Supposed that is the universe of discourse and
is the element of , therefore, a crisp set can be defined as a set which consists of different elements ( ) will all or no membership in a set. A fuzzy set is a set that each element has a membership grade, therefore it can be written by the following definition;
*()|+(25) Where an element of universe of discourse is, is the membership function (MF) of fuzzy set. The membership function ( ()) of fuzzy set must have a value between zero and one. If the membership function ( ) value equal to zero or one, this set
change to a crisp set but if it has a value between zero and one, it is a fuzzy set. Defining membership function for fuzzy sets has divided into two main groups; namely; numerical and functional method, which in numerical method each number has different degrees of membership function and functional method used standard functions in fuzzy sets. The membership function which is often used in practical applications includes triangular form, trapezoidal form, bell-shaped form, and Gaussian form.
Linguistic variable can open a wide area to use of fuzzy logic theory in many applications (e.g., control and system identification). In a natural artificial language all numbers replaced by words or sentences.
Rule statements are used to formulate the condition statements in fuzzy logic. A single fuzzy rule can be written by
(26) where and are the Linguistic values that can be defined by fuzzy set, the of the part of is called the antecedent part and the of the part of is called the Consequent or Conclusion part. The antecedent of a fuzzy if-then rule can have multiple parts, which the following rules shows the multiple antecedent rules:
(27) where is error, ?is change of error, is Negative Big, is Medium Left, is torque and is Large Left. rules have three parts, namely, fuzzify inputs, apply fuzzy operator and apply implication method which in fuzzify inputs the fuzzy statements in the antecedent replaced by the degree of membership, apply fuzzy operator used when the antecedent has multiple parts and replaced by single number between 0 to 1, this part is a degree of support for the fuzzy rule, and apply implication method used in consequent of fuzzy rule to replaced by the degree of membership. The fuzzy inference engine offers a mechanism for transferring the rule base in fuzzy set which it is divided into two most important methods, namely, Mamdani method and Sugeno method. Mamdani method is one of the common fuzzy inference systems and he designed one of the first fuzzy controllers to control of system engine. Mamdani’s fuzzy inference system is divided into four major steps: fuzzification, rule evaluation, aggregation of the rule outputs and defuzzification. Michio Sugeno use a singleton as a membership function of the rule consequent part. The following definition shows the Mamdani and Sugeno fuzzy rule base
()
(28)
When and have crisp values fuzzification calculates the membership degrees for antecedent part. Rule evaluation focuses on fuzzy operation ( ) in the antecedent of the fuzzy rules. The aggregation is used to calculate the output fuzzy set and several methodologies can be used in fuzzy logic controller aggregation, namely, Max-Min aggregation, Sum-Min aggregation, Max-bounded product, Max-drastic product, Max-bounded sum, Max-algebraic sum and Min-max. Two most common methods that used in fuzzy logic controllers are Max-min aggregation and Sum-min aggregation. Max-min aggregation defined as below
()
?
()
20()()13
(29)
The Sum-min aggregation defined as below
()
?
()
∑0()()1
(30)
where is the number of fuzzy rules activated by and and also ?() is a fuzzy interpretation of rule. Defuzzification is the last step in the fuzzy inference system which it is used to transform fuzzy set to crisp set. Consequently defuzzification’s input is the aggregate output and the defuzzification’s output is a crisp number. Centre of gravity method ( ) and Centre of area method ( ) are two most common defuzzification methods, which method used the following equation to calculate the defuzzification
()∑∑( )
∑∑( )
(31)
and method used the following equation to calculate the defuzzification
()∑( )
∑( )
(32)
Where () and () illustrates the crisp value of defuzzification output, is discrete element of an output of the fuzzy set, ( ) is the fuzzy set membership function, and is the number
of fuzzy rules.
Based on foundation of fuzzy logic methodology;
fuzzy logic controller has played important rule to design nonlinear controller for nonlinear and uncertain systems [53-66]. However the application area for fuzzy control is really wide, the basic form for all command types of controllers consists of;
?Input fuzzification (binary-to-fuzzy[B/F]conversion) ?Fuzzy rule base (knowledge base)
?Inference engine
?Output defuzzification (fuzzy-to-binary [F/B]conversion).
Linear Controller:In the absence of spherical motor knowledge, proportional-integral-derivative (PID), proportional-integral (PI) and proportional -derivative (PD) may be the best controllers, because they are model-free, and they’re parameters can be adjusted easily and separately [1] and it is the most used in spherical motor. In order to remove steady-state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. PD control guarantees stability only when the PD gains tend to infinity, the tracking error does not tend to zero when friction and gravity forces are included in the spherical motor dynamics [2]. Model-based compensation for PD control is an alternative method to substitute PID control [1], such as adaptive gravity compensation [3], desired gravity compensation [2], and PD+ with position measurement [4]. They all needed structure information of the spherical motor dynamic formulation. Some nonlinear PD controllers can also achieve asymptotic stability, for example PD control with time-varying gains [5], PD control with nonlinear gains [6], and PD control with feedback linearization compensation [8]. But these controllers are complex; many good properties of the linear PID control do not exist because these controllers do not have the same form as the industrial PID. Design of a linear methodology to control of spherical motor was very straight forward. Since there was an output from the torque model, this means that there would be two inputs into the PID controller. Similarly, the outputs of the controller result from the two control inputs of the torque signal. In a typical PID method, the controller corrects the error between the desired input value and the measured value. Since the actual position is the measured signal.
()()()(33)
∑(34) The model-free control strategy is based on the assumption that the orientation of the spherical motor are all independent and the system can be decoupled into a group of single-axis control systems [14-16]. Therefore, the kinematic control method always results in a group of individual controllers, each for an active rotation of the spherical. With the independent orientation assumption, no a priori knowledge of spherical motor dynamics is needed in the kinematic controller design, so the complex computation of its dynamics can be avoided and the controller design can be greatly simplified. This is suitable for real-time control applications when powerful processors, which can execute complex algorithms rapidly, are not accessible. However, since joints coupling is neglected, control performance degrades as operating speed increases and a spherical motor controlled in this way is only appropriate for relatively slow motion [13-16]. The fast motion requirement results in even higher dynamic coupling between the various spherical motor orientations, which cannot be compensated for by a standard motor controller such as PID [16], and hence model-based control becomes the alternative.
III.Methodology
Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic
system’s parameters. This controller is used in seve ral applications such as in robotics, process control, aerospace and power electronics. Sliding mode controller is used to control of nonlinear dynamic systems particularly for spherical motor, because it has a suitable control performance and it is a robust and stable. Conversely pure sliding mode controller is a high-quality nonlinear controller; it has two important problems; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. To reduce the chattering phenomenon and equivalent dynamic problems, this research is focused on applied parallel fuzzy logic theorem and modified linear methodology in sliding mode controller as a compensator. In a typical PD method, the controller corrects the error between the desired input value and the measured value. Since the actual position is the measured signal. The derivative part of PD methodology is worked based on change of error and the derivative coefficient. In this research the modified PD is used based on boundary derivative part. Based on the SMC controller;
?()(35) ()() ( )(36)
(37) This is suitable for real-time control applications when powerful processors, which can execute complex algorithms rapidly, are not accessible. The result of modified PD method shows the power of disturbance rejection in this methodology.
Fuzzy logic theory is used in parallel with sliding mode controller to compensate the limited uncertainty in system’s dynamic. In this method fuzzy logic theorem is applied to sliding mode controller to remove the nonlinear uncertainty part which it is bas ed on nonlinear dynamic formulation. To achieve this goal, the dynamic equivalent part of pure sliding mode controller is modeled by Mamdani’s performance/ error-based fuzzy logic methodology. Another researcher’s method is based on applied fuzzy logic theorem in sliding mode controller to design a fuzzy model-based controller. This technique was employed to obtain the desired control behavior with a number of information about dynamic model of system and a fuzzy switching control was applied to reinforce system performance. Reduce or eliminate the chattering phenomenon and reduce the error are played important role, therefore switching method is used beside the artificial intelligence part to solve the chattering problem with respect to reduce the error. Equivalent part of sliding mode controller is based on nonlinear dynamic formulations of spherical motor. Spherical motor’s dynamic formulations are highly nonlinear and some of parameters are unknown therefore design a controller based on dynamic formulation is complicated. To solve this challenge parallel fuzzy logic methodology is applied to sliding mode controller. In this method fuzzy logic method is used to compensate some dynamic formulation that they are used in equivalent part. To solve the challenge of sliding mode controller based on nonlinear dynamic formulation this research is focused on compensate the nonlinear equivalent formulation by parallel fuzzy logic controller. In this method; dynamic nonlinear equivalent part is modeled by performance/error-based fuzzy logic controller. In this method; error based Mamdani’s fuzzy inference system has considered with two inputs, one output and totally 49 rules. For both sliding mode controller and parallel fuzzy inference system plus sliding mode controller applications the system performance is sensitive to the sliding surface slope coefficient( ). For instance, if large value of is chosen the response is very fast the system is unstable and conversely, if small value of is considered the response of system is very slow but system is stable. Therefore to have a good response, compute the best value sliding surface slope coefficient is very important. In parallel fuzzy inference system compensator of sliding mode controlle r the PD-sliding surface is defined as follows:
(38) where ,-. The time derivative of S is computed;
(39)
The parallel fuzzy error-based compensator of sliding mode controller’s output is written;
?(40) Based on fuzzy logic methodology
()∑ ( )(41)
where is adjustable parameter (gain updating factor) and ( )is defined by;
()∑ ( )
∑ ( )
(42)
Design an error-based parallel fuzzy compensate of equivalent part based on Mamdani’s fuzzy inference method has four steps, namely, fuzzification, fuzzy rule base and rule evaluation, aggregation of the rule output (fuzzy inference system) and defuzzification.
Fuzzification: the first step in fuzzification is determine inputs and outputs which, it has two inputs ( ?)and one output (). The inputs are error (e) which measures the difference between desired and actual output, and the change of error ( ?)which measures the difference between desired and actual velocity and output is fuzzy equivalent torque. The second step i s chosen an appropriate membership function for inputs and output which, to simplicity in implementation because it is a linear function with regard to acceptable performance triangular membership function is selected in this research. The third step is chosen the correct labels for each fuzzy set which, in this research namely as linguistic variable. Based on experience knowledge the linguistic variables for error (e) are; Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM), Positive Big (PB), and experience knowledge it is quantized into thirteen levels represented by: -1, -0.83, -0.66, -0.5, -0.33, -0.16, 0, 0.16, 0.33, 0.5, 0.66, 0.83, 1 the linguistic variables for change of error ( ?) are; Fast Left (FL), Medium Left (ML), Slow Left (SL),Zero (Z), Slow Right (SR), Medium Right (MR), Fast Right (FR), and it is quantized in to thirteen levels represented by: -6, -5, -0.4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, and the linguistic variables to find the output are; Large Left (LL), Medium Left (ML), Small Left (SL), Zero (Z), Small Right (SR), Medium Right (MR), Large Right (LR) and it is quantized in to thirteen levels represented by: -85, -70.8, -56.7, -42.5, -28.3, -14.2, 0, 14.2, 28.3, 42.5, 56.7, 70.8, 85.
Fuzzy rule base and rule evaluation: the first step in rule base and evaluation is to provide a least structured method to derive the fuzzy rule base which, expert experience and control engineering knowledge is used because this method is the least structure of the other one and the researcher derivation the fuzzy rule base from the knowledge of system operate and/or the classical controller. Design the rule base of fuzzy inference system can play important role to design the best performance of parallel fuzzy plus sliding mode controller, that to calculate the fuzzy rule base the researcher is used to heuristic method which, it is based on the behavior of the control of robot manipulator. The complete rule base for this controller is shown in Table 1. Rule evaluation focuses on operation in the antecedent of the fuzzy rules in fuzzy sliding mode controller. This part is used fuzzy operation in antecedent part which operation is used. Aggregation of the rule output (Fuzzy inference): based on fuzzy methodology, Max-Min aggregation is used in this work (see table 1).
Table 1: Modified Fuzzy rule base table
Defuzzification: The last step to design fuzzy inference in our parallel fuzzy compensator plus sliding mode controller is defuzzification. This part is used to transform fuzzy set to crisp set, therefore the input for defuzzification is the aggregate output and the output of it is a crisp number. Based on fuzzy methodology Center of gravity method ( ) is used in this research. Table 2 shows the lookup table in parallel fuzzy compensator sliding mode controller which is computed by COG defuzzification method. Table 2 has 169 cells to shows the error-based fuzzy compensate of
equivalent part behavior (see table 2).
Table 2: performance: lookup table in parallel fuzzy compensate of sliding mode controller by COG Proof of stability in modified PD fuzzy-based tuning
error-based fuzzy sliding mode controller: The
Lyapunov function in this design is defined as
∑
(43)
where is a positive coefficient, , is
minimum error and is adjustable parameter. Since
is skew-symetric matrix;
( ) (44)
The controller formulation is defined by
??(45)
According to (44)
()()??(46)
Since
()(47)
The derivation of V is defined
∑(48)
( )∑
Based on (46) and (47)
( )
∑
(49)
where ,() ?( ?) ?-∑()
∑[( )]
∑
suppose is defined as follows
∑, ()-
, ()-
()
(50)
Where (), ()()()()-
()
()( )
∑()( )
(51)
where ( ) is membership function.
The fuzzy system is defined as
()∑()()(52)
where ( ) is adjustable
parameter in (51)
∑[( ( )]
∑
(53)
Based on
∑[( ()
()]∑
(54)
∑[( ( )()]
∑, ()-)
where()is adaption law,
()
is considered by
∑, .( )()/-
(55)
The minimum error is defined by
.( )()/(56) Therefore is computed as
∑, -
(57)
∑| || |
∑| || |
∑| |(||)
(58)
For continuous function ( ), and suppose it is defined the fuzzy logic system in form of
|()()|(59)
The minimum approximation error ( ) is very small.
||(
)()
(60)
IV.Results
Modified fuzzy compensator sliding mode controller is implemented in MATLA B/SIMULINK environment. Tracking performance and disturbance rejection is compared for circle trajectory.
Tracking performances: From the simulation for first, second and third joints (spherical joints) without any disturbance, it was seen that proposed controller has a good trajectory performance, because thi s controller is adjusted and worked on certain environment. Figure 1 shows the tracking performance in certain system and without external disturbance this controller.
Fig. 1: Proposed Methodology applied to spherical motor without
disturbance
Disturbance rejection:Figures 2 and 3 show the power disturbance elimination in pure sliding mode controller and proposed method. The main targets in these controllers are disturbance rejection as well as the other responses. A band limited white noise with predefined of 40% the power of input signal is applied to controllers. It found fairly fluctuations in SMC trajectory responses. Among following graphs relating to trajectory following with external disturbance, SMC has fairly fluctuations.
Fig. 2: SMC in presence of uncertainty and external disturbance:
applied to spherical motor
Fig. 3: Proposed method in presence of uncertainty and external disturbance: applied to spherical motor
V.Conclusion
Based on the dynamic formulation of spherical motor it is clear that; this system is highly nonlinear and uncertain dynamic parameters. Control of this system based on classical methodology is very complicated. The main contributions of this paper is compensating the nonlinear model base controller by nonlinear artificial intelligence model-free compensator and improve the stability based on modified PD methodology. The structure of modified PD compensator sliding mode controller with parallel fuzzy inference compensator is new. We propose parallel structure and chattering free compensator: parallel compensation and chattering free method is important challenge and to have the better performance modified PD and fuzzy logic method is introduced. The stability analysis of parallel fuzzy compensator plus sliding mode controller is test via Lyapunov methodology. The benefits of the proposed method; the chattering effects of parallel fuzzy inference compensator plus sliding mode controller, the slow convergence of the fuzzy and the chattering problem of sliding mode method are avoided effectively.
Acknowledgment
The authors would like to thank the anonymous reviewers for their careful reading of this paper and for their helpful comments. This work was supported by the Institute of Advanced Science and Technology (IRANSSP) Research and Development Corporation Program of Iran under grant no. 2013-Persian Gulf-2.B. References
[1] Vachtsevanos, G. I., Davey, K. and Lee, K. M.,
"Development of a Novel Intelligent Robotic Manipulator," IEEE Control System Magazine, 1987, pp.9-15.
[2] Davey, K., Vachtsevanos, G. I., and Powers, R.,
"An analysis of Fields and Torques in Spherical Induction Motors," lEE Transactions on Magnetics, Vol. MAG-23, 1987, pp. 273-282.
[3] Foggia, A., Oliver, E., Chappuis, F., "New Three
Degrees of Freedom Electromagnetic Actuator,"
Conference Record -lAS Annual Meeting, Vol. 35, New York, 1988.
[4] Lee, K. M., Vachtsevanos, G. and Kwan, C-K.,
"Development of a Spherical Wrist Stepper Motor," Proceedings of the 1988 IEEE lntemational Conference on Robotics and Automation, Philadelphia, PA. April 26-29.
[5] Lee, K. M., Pei. I., "Kinematic Analysis of a Three
Degree-of-Freedom Spherical Wrist Actuator,"
The Fifth International Conference on Advanced Robotics, Italy,1991.
[6] Wang, I., Jewel, G., Howe, D., "Modeling of a
Novel Spherical Pennanent Magnet Actuator,"
Proceedings of IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico, pp 1190-1195, 1997.
[7] Wang, I., Jewel, G., Howe, D., "Analysis, Design
and Control of a Novel Spherical Pennanent Magnet Actuator," lEE Proceedings on Electrical Power Applications., vol. 154, no. 1, 1998.
[8] Chirikjian, G. S., and Stein, D., "Kinematic Design
and Commutation of a Spherical Stepper Motor,"
IEEEIASME Transactions on Mechatronics, vol. 4, n 4, Piscataway, New Jersey, pp. 342-353, Dec.
1999.
[9] Kahlen, K., and De Doncker, R. W., "CW'l'ent
Regulators for Multi-phase Pennanent Magnet Spherical Machines." Industry Applications Conference Record of the 2000 IEEE, vol. 3, 2000, pp. 2011-2016.
[10] Lee, K. M., Pei, I., and Gilboa, U., "On the
Development of a Spherical Wrist Actuator,"
Proceedings of the 16th NSF Conference on Manufacturing Systems Research, Tempe AZ, January 8-12, 1990.
[11] Yang, C., Back, Y. S., "Design and Control of the
3-dcgn:es of freedom actuator by Controlling the Electromagnetic Force," IEEE Transactions on Magnetics, May, 1999, pp. 3607-3609.
[12]Samira Soltani & Farzin Piltan, “Design Artificial
Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain”, World Applied Science Journal (WASJ), 14 (9): 1306-1312,
2011.
[13]Farzin Piltan, Mohammadali Dialame, Abbas Zare
& Ali Badri,“Design Novel Lookup Table Changed Auto Tuning FSMC:Applied to Robot Manipulator”, Intern ational Journal of Engineering,
6 (1):25-41, 2012
[14]Farzin Piltan, Mohammad Keshavarz, A li Badri &
Arash Zargari,“Design Novel Nonlinear Controller Applied to RobotManipulator: Design New Feedback Linearization Fuzzy Controller with Minimum Rule Base Tuning Method”, International Journal of Robotics and Automation,3 (1):1-12, 2012
[15]Farzin Piltan, Iman Nazari, Sobhan Siamak,
Payman Ferdosali,“Methodology of FPGA-Based Mathematical error-Based Tuning Sliding Mode Controller”, International Journal of Control and Automation, 5(1), 89-118, 2012
[16]Farzin Piltan, Bamdad Boroomand, Arman Jahed
& Hossein Rezaie, “Methodology of Mathematical Error-Based Tuning Sliding Mode Controller”, International Journal of Engineering, 6 (2):96-117, 2012
[17]Farzin Piltan, Sara Emamzadeh, Zahra Hivand,
Fatemeh Shahriyari & Mina Mirazaei, ”PUMA-
560 Robot Manipulator Position Sliding Mode Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate/Undergraduate Nonlinear Control, Robotics and MATLAB Courses”, International Jou rnal of Robotics and Automation, 3(3):106-150, 2012
[18]Farzin Piltan, A li Hosainpour, Ebrahim Mazlomian,
Mohammad Shamsodini, Mohammad H.
Yarmahmoudi, ”Online Tuning Chattering Free Sliding Mode Fuzzy Control Design: Lyapunov Approach”, International Journal of Robotics and Automation, 3(3):77-105, 2012
[19]Farzin Piltan, Mina Mirzaei, Forouzan Shahriari,
Iman Nazari, Sara Emamzadeh, “Design Baseline Computed Torque Controller”, International Journal of Engineering, 6(3): 129-141, 2012 [20]Farzin Piltan, Mohammad H. Yarmahmoudi,
Mohammad Shamsodini, Ebrahim Mazlomian, Ali Hosainpour, ”PUMA-560 Robot Manipulator Position Computed Torque Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate Nonlinear Control and MATLA B Courses”, International Journal of Robotics and Automation, 3(3): 167-191, 2012
[21]Farzin Piltan, Hossein Rezaie, Bamdad
Boroomand, Arman Jahed, “Design Robust Backstepping on-line Tuning Feedback Linearization Control Applied to IC Engine”, International Journal of Advance Science and Technology, 11:40-22, 2012
[22]Farzin Piltan, Mohammad R. Rashidian,
Mohammad Shamsodini and Sadeq Allahdadi,
Effect of Rule Base on the Fuzzy-Based Tuning Fuzzy Sliding Mode Controller: Applied to 2nd Order Nonlinear System”, International Journal of Advanced Science and Technology, 46:39-70, 2012
[23]Farzin Piltan, Arman Jahed, Hossein Rezaie and
Bamdad Boroomand, ”Methodology of Robust Linear On-line High Speed Tuning for Stable Sliding Mode Controller: Applied to Nonlinear System”, International Journal of Control and Automation, 5(3): 217-236, 2012
[24]Farzin Piltan, Bamdad Boroomand, Arman Jahed
and Hossein Rezaie, ”Performance-Based Adaptive Gradient Descent Optimal Coefficient Fuzzy Sliding Mode Methodology”, International Journal of Intelligent Systems and Applications, , vol.4, no.11, pp.40-52, 2012.
[25]Farzin Piltan, Mehdi A kbari, Mojdeh Piran ,
Mansour Bazregar, ”Design Model Free Switching Gain Scheduling Baseline Controller with Application to Automotive Engine”, International Journal of Information Technology and Computer Science, vol.5, no.1, pp.65-73, 2013.DOI:
10.5815/ijitcs.2013.01.07.
[26]Farzin Piltan, Mojdeh Piran , Mansour Bazregar,
Mehdi Akbari, “Design High Impact Fuzzy Baseline Variable Structure Methodology to Artificial Adjust Fuel Ratio”, International Journal of Intelligent Systems and Applications, vol.5, no.2, pp.59-70, 2013.DOI: 10.5815/ijisa.2013.02.0.
[27]Farzin Piltan, M. Bazregar, M. kamgari, M. Akbari
and M. Piran, “Adjust the Fuel Ratio by High Impact Chattering Free Sliding Methodology with Application to Automotive Engine”,International Journal of Hybrid Information Technology, 6(1), 2013.
[28]Farzin Piltan, S. Zare , F. ShahryarZadeh, M.
Mansoorzadeh, M.kamgari, “Supervised Optimization of Fuel Ratio in IC Engine Based on Design Baseline Computed Fuel Methodology”, International Journal of Information Technology and Computer Science, vol.5, no.4, pp.76-84, 2013.DOI: 10.5815/ijitcs.2013.04.09.
[29]Farzin Piltan, M. Mansoorzadeh, S. Zare,
F.Shahryarzadeh, M. Akbari, “A rtificial Tune of
Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control”, International Journal of Electrical and Computer Engineering, 3(2), 2013.
[30]M. Bazregar, Farzin Piltan, A. Nabaee and M.M.
Ebrahimi, “Parallel Soft Com puting Control Optimization Algorithm for Uncertainty Dynamic Systems”, International Journal of Advanced Science and Technology, 51, 2013.
[31]Farzin Piltan, M.H. Yarmahmoudi, M. Mirzaei, S.
Emamzadeh, Z. Hivand, “Design Novel Fuzzy
Robust Feedback Linearization Control with
Application to R obot Manipulator”, International Journal of Intelligent Systems and Applications, vol.5, no.5, pp.1-10, 2013.DOI:
10.5815/ijisa.2013.05.01.
[32]Sh. Tayebi Haghighi, S. Soltani, Farzin Piltan, M.
kamgari, S. Zare, “Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller Vs. Sliding Mode Controller”, International Journal of Intelligent Systems and
Applications, vol.5, no.6, pp.78-88, 2013.DOI:
10.5815/ijisa.2013.06.10.
[33]Amin Jalali, Farzin Piltan, M. Keshtgar, M. Jalali,
“Colonial Competitive Optimization Sliding Mode
Controller with Application to Robot Manipulator”, International Journal of Intelligent Systems and
Applications, vol.5, no.7, pp.50-56, 2013. DOI:
10.5815/ijisa.2013.07.07.
[34]Salehi, Farzin Piltan, M. Mousavi, A. Khajeh, M.
R. Rashidian, “Intelligent Robust Feed-forward Fuzzy Feedback Linearization Estimation of PID
Control with Application to Continuum Robot”,
International Journal of Information Engineering and Electronic Business, vol.5, no.1, pp.1-16, 2013.
DOI: 10.5815/ijieeb.2013.01.01.
[35]Farzin Piltan, M.J. Rafaati, F. Khazaeni, A.
Hosainpour, S. Soltani, “A Design High Impact
Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator”, International Journal of Information Engineering and Electronic Business, vol.5, no.1, pp.17-25, 2013. DOI:
10.5815/ijieeb.2013.01.02.
[36]Amin Jalali, Farzin Piltan, A. Gavahian, M. Jalali,
M. Adibi, “Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot manipulator”, International Journal of Information Engineering and Electronic Business,
vol.5, no.1, pp.68-78, 2013. DOI:
10.5815/ijieeb.2013.01.08.
[37]Farzin Piltan, F. ShahryarZadeh,M.
Mansoorzadeh,M.kamgari,S. Zare, “Robust Fuzzy PD Method with Parallel Computed Fuel
Ra tio Estimation Applied to Automotive Engine“,
International Journal of Intelligent Systems and
Applications, vol.5, no.8, pp.83-92, 2013. DOI:
10.5815/ijisa.2013.08.10.
[38]Farzin Piltan, A. Nabaee, M.M. Ebrahimi, M.
Bazregar, “Design Robust Fuzzy Sliding Mode
Control Technique for Robot Manipulator Systems
with Modeling Uncertainties”, International Journal of Information Technology and Computer
Science, vol.5, no.8, pp.123-135, 2013. DOI:
10.5815/ijitcs.2013.08.12.
[39]Farzin Piltan, M. Mansoorzadeh,M.Akbari,S.
Zare, F. ShahryarZadeh “Management of Environmental Pollution by Intelligent Control of
Fuel in an Internal Combustion Engine“ Global Journal of Biodiversity Science And Management, 3(1), 2013.
[40]M. M. Ebrahimit Farzin Piltan, M. Bazregar and
A.R. Nabaee, “Intelligent Robust Fuzzy-Parallel
Optimization Control of a Continuum Robot
Manipulator”, International Journal of Control and Automation, 6(3), 2013.
[41]O.R. Sadrnia, Farzin Piltan, M. Jafari, M. Eram
and M. Shamsodini, “Design PID Estimator Fuzzy plus Backstepping to Control of Uncertain Continuum Robot”, International Journal of Hybrid Information Technology, 6(4), 2013. [42]AminJalali, Farzin Piltan, H. Hashemzadeh, A.
Hasiri, M.R Hashemzadeh, “Design Novel Soft Computing Backstepping Controller with Application to Nonlinear Dynamic Uncertain System”, International Journal of Intelligent Systems and Applications, vol.5, no.10, pp.93-105, 2013. DOI: 10.5815/ijisa.2013.10.12.
[43]M. Moosavi, M. Eram, A. Khajeh, O. Mahmoudi
and Farzin Piltan, “Design New Artific ial Intelligence Base Modified PID Hybrid Controller for Highly Nonlinear System”, International Journal of Advanced Science and Technology, 57,
2013.
[44]S. Zahmatkesh, Farzin Piltan, K. Heidari, M.
Shamsodini, S. Heidari, “Artificial Error Tuning Based on Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control”
International Journal of Intelligent Systems and
Applications, vol.5, no.11, pp.34-46, 2013. DOI:
10.5815/ijisa.2013.11.04.
[45]S. Heidari, Farzin Piltan, M. Shamsodini, K.
Heidari and S. Zahmatkesh, “Design New Nonlinear Controller with Parallel Fuzzy Inference System Compensator to Control of Continuum
Robot Manipulator”,International Journal of Control and Automation, 6(4), 2013.
[46]FarzinPiltan, M. Kamgari, S. Zare, F.
ShahryarZadeh, M. Mansoorzadeh, “Design Novel Model Reference Artificial Intelligence Based Methodology to Optimized Fuel Ratio in IC Engine”, International Journal of Information Engineering and Electronic Business, vol.5, no.2, pp.44-51, 2013. DOI: 10.5815/ijieeb.2013.02.07. [47]Farzin Piltan, Mehdi Eram, Mohammad Taghavi,
Omid Reza Sadrnia, Mahdi Jafari,"Nonlinear Fuzzy Model-base Technique to Compensate Highly Nonlinear Continuum Robot Manipulator",
IJISA, vol.5, no.12, pp.135-148, 2013. DOI:
10.5815/ijisa.2013.12.12
[48]Amin Jalali, Farzin Piltan, Mohammadreza
Hashemzadeh, Fatemeh BibakVaravi, Hossein Hashemzadeh,"Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for
Continuum Robot", IJITCS, vol.5, no.12, pp.97-112, 2013. DOI: 10.5815/ijitcs.2013.12.12
[49]Farzin Piltan, A. Hosainpour, S. Emamzadeh, I.
Nazari, M. Mirzaie, “Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator”, International Journal of Robotics and Automation, Vol. 2, No. 4, December 2013, pp. 149~162. [50]Farzin Piltan, Mahdi Jafari, Mehdi Eram, Omid
Mahmoudi, Omid Reza Sadrnia, "Design Artificial Intelligence-Based Switching PD plus Gravity for Highly Nonlinear Second Order System", International Journal of Engineering and Manufacturing, vol.3, no.1, pp.38-57, 2013.DOI:
10.5815/ijem.2013.01.04
[51]Farzin Piltan, Sara Emamzadeh, Sara Heidari,
Samaneh Zahmatkesh, Kamran Heidari, "Design Artificial Intelligent Parallel Feedback Linearization of PID Control with Application to Continuum Robot", International Journal of Engineering and Manufacturing, vol.3, no.2, pp.51-72, 2013.DOI: 10.5815/ijem.2013.02.04 [52]Mohammad Mahdi Ebrahimi, Farzin Piltan,
Mansour Bazregar, AliReza Nabaee,"Artificial Chattering Free on-line Modified Sliding Mode Algorithm: Applied in Continuum Robot Manipulator", International Journal of Information Engineering and Electronic Business, vol.5, no.5, pp.57-69, 2013. DOI: 10.5815/ijieeb.2013.05.08 [53]Arman Jahed, Farzin Piltan, Hossein Rezaie,
Bamdad Boroomand, "Design Computed Torque Controller with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator", International Journal of Information Engineering and Electronic Business, vol.5, no.3, pp.66-77, 2013. DOI: 10.5815/ijieeb.2013.03.08
Authors’ Profiles
AliReza Siahbazi is currently
working as a co researcher in
Control and Robotic Lab at the
institute of advance science and
technology, IRAN SSP research
and development Center. He is a
Master in field of Computer
Engineering from Shiraz
University, Shiraz, IRA N. His current research interests are in the area of nonlinear control, artificial control system and robotics, and spherical motor.
Ali Barzegar is currently working as a co researcher in Control and Robotic Lab at the institute of advance science and technology, IRA N SSP research and development Center. His current research interests are
in the area of nonlinear control,
artificial control system and robotics,
and spherical motor.
Mahmood Vosoogh is currently
working as a co researcher in
Control and Robotic Lab at the
institute of advance science and
technology, IRA N SSP research
and development Center. His
current research interests are in the
area of nonlinear control, artificial
control system and robotics, and spherical motor.
Abdol Majid Mirshekaran is
currently working as a co
researcher in Control and
Robotic Lab at the institute of
advance science and technology,
IRA N SSP research and
development Center. He is a
Master in field of Electrical
Engineering from Islamic Azad
University, IRAN. His current research interests are in the area of nonlinear control, artificial control system and robotics, and spherical motor.
Samira Soltani is currently
working as assistant researcher in
Control and Robotic Lab, institute
of advance science and
technology, IRAN SSP research
and development Center. In 2009
she is jointed the Control and
Robotic Lab, institute of advance
science and technology, IRAN SSP, Shiraz, IRAN. In addition to do some projects, Samira Soltani is the main author of more than 8 scientific papers in refereed journals. Her current research interests are in the area of nonlinear control, artificial control system, robotics and spherical motor.
How to cite this paper: Alireza Siahbazi, Ali Barzegar, Mahmood Vosoogh, Abdol Majid Mirshekaran, Samira Soltani,"Design Modified Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Spherical Motor", IJISA, vol.6, no.3, pp.12-25, 2014. DOI:
10.5815/ijisa.2014.03.02
PWM控制直流电机的系统的设计
电力电子与电机拖动综合课程设计 题目： PWM控制直流电机的系统 专业： 05自动化 学号： 200510320219 姓名：张建华 完成日期： 指导教师：李晓高
电力电子与电机拖动综合课程设计任务书 班级：自动化05 姓名：张建华指导老师：2008年6月10日 年月日
目录
1 引言 直流电机由于具有速度控制容易，启、制动性能良好，且在宽范围内平滑调速等特点而在冶金、机械制造、轻工等工业部门中得到广泛应用。直流电动机转速的控制方法可分为两类，即励磁控制法与电枢电压控制法。励磁控制法控制磁通，其控制功率虽然小，但低速时受到磁饱和的限制，高速时受到换向火花和换向器结构强度的限制;而且由于励磁线圈电感较大，动态响应较差。所以常用的控制方法是改变电枢端电压调速的电枢电压控制法。调节电阻R即可改变端电压，达到调速目的。但这种传统的调压调速方法效率低。随着电力电子技术的进步，发展了许多新的电枢电压控制方法，其中PWM(脉宽调制)是常用的一种调速方法。其基本原理是用改变电机电枢(定子)电压的接通和断开的时间比(占空比)来控制马达的速度，在脉宽调速系统中，当电机通电时，其速度增加；电机断电时，其速度减低。只要按照一定的规律改变通、断电的时间，即可使电机的速度达到并保持一稳定值。最近几年来，随着微电子技术和计算机技术的发展及单片机的广泛应用，使调速装置向集成化、小型化和智能化方向发展。 本电机调速系统采用脉宽调制方式, 与晶闸管调速相比技术先进, 可减少对电源的污染。为使整个系统能正常安全地运行, 设计了过流、过载、过压、欠压保护电路, 另外还有过压吸收电路。确保了系统可靠运行。 2 系统概述 2.1 系统构成 本系统主要有信号发生电路、PWM速度控制电路、电机驱动电路等几部分组成。整个系统上采用了转速、电流双闭环控制结构，如图1所示。在系统中设置两个调节器，分别调节转速和电流，二者之间实行串级连接，即以转速调节器
四相步进电机控制系统设计资料讲解
四相步进电机控制系 统设计
课题：四相五线单4拍步进制电动机的正反转控制专业：机械电子工程 班级：2班 学号： 20110259 姓名：周后银 指导教师：李立成 设计日期： 2014.6.9~2014.6.20 成绩：
1概述 本实验旨在通过控制STC89C52芯片，实现对四相步进电机的转动控制。具体功能主要是控制电机正转10s、反转10s，连续运行1分钟，并用1602液晶显示屏显示出来。 具体工作过程是：给系统上电后，按下启动开关，步进电机按照预先 实验具体用到的仪器：STC89C52芯片、开关单元、四项步进电机、等硬件设 备。 实验具体电路单元有：单片机最小系统、步进电机连接电路、开关连接电路、1602液晶显示屏显示电路。 2四相步进电机 2.1步进电机 步进电机是一种将电脉冲转化为角位移的执行机构。电机的转速、停止的位置只取决于脉冲信号的频率和脉冲数，而不受负载变化的影响，即给电机加一个脉冲信号，电机则转过一个步距角。 2.2步进电机的控制 1.换相顺序控制：通电换相这一过程称为脉冲分配。 2.控制步进电机的转向控制：如果给定工作方式正序换相通电，步进 电机正转，如果按反序通电换相，则电机就反转。
3.控制步进电机的速度控制：如果给步进电机发一个控制脉冲，它就 转一步，再发一个脉冲，它会再转一步。两个脉冲的间隔越短，步进电机就转得越快。 2.3步进电机的驱动模块 ABCD四相工作指示灯指示四相五线步进电机的工作状态 2.4步进电机的工作过程 开关SB接通电源，SA、SC、SD断开，B相磁极和转子0、3号齿对齐，同时，转子的1、4号齿就和C、D相绕组磁极产生错齿，2、5号齿就和D、A相绕组磁极产生错齿。当开关SC接通电源，SB、SA、SD断开时，由于C相绕组的磁力线和1、4号齿之间磁力线的作用，使转子转动， 1、4号齿和C相绕组的磁极对齐。而0、3号齿和A、B相绕组产生错齿，
基于PLC的电动机制动控制系统设计
目录 课程设计任务书 (1) 1 课题介绍 (2) 1.1 题目 (2) 1.2 背景介绍 (2) 2 总体方案设计 (4) 2.1 设计目的 (4) 2.2 控制要求 (4) 2.3 设计要求 (4) 3 硬件设计 (4) 3.1 硬件方案框图 (4) 3.2 硬件选型 (5) 3.3 主电路原理图的设计 (6) 3.4 控制电路原理图的设计 (6) 4 软件设计及调试 (8) 4.1 控制系统的I/O点及地址分配 (8) 4.2系统工作流程框图 (8) 4.3 系统调试 (10) 5 总结 (12) 参考文献 (14) 附录 (16)
安徽农业大学经济技术学院 《电气控制与可编程控制器》课 程 设 计 任 务 书 题目 基于PLC 的电动机制动控制系统设计与调试 专业、班级 电气08-2 班 学号 2008010202 姓名 主要内容、基本要求、主要参考资料等： 一、主要内容 1.熟悉题目、收集资料，充分了解技术要求，明确设计任务； 2.总体设计。正确选定设计方案，画出系统总体结构框图； 3.硬件设计。选择电器元器件，确定电器元器件明细表。用CAD 画出电气原理图，并作简要分析； 4.软件设计。根据控制要求确定I/O 分配表，画出系统工作流程图，设计程序及编写程序说明，给出编程原件明细表等； 5.系统调试； 6.整理编写课程设计说明书。 二、课题要求 1.控制要求 三相笼型异步电动机具有反接制动电阻的可逆运行反接制动控制。 2.设计要求 1）控制系统采用PLC 来实现； 2）提供短路、过载、联锁等保护措施； 3）具有紧急停车功能； 三、基本要求 1．根据题意, 用CAD 画出电气原理图和PLC 端子接线图。设计要合理，画图要规范标准。 2．完成程序的编写工作,并利用模拟器和实验室设备完成调试工作。 3. 完成课程设计说明书一份，阐明设计任务与依据，设计原则、方法、设计方案与成果，并力求论证充分、简明通顺、条理清晰、逻辑性强。 四、主要参考文献 王永华.现代电气控制及PLC 应用技术.北京航空航天大学出版社. 指导教师签名： 课程负责人签名： 2012年 5 月 10日 学院： 专业班级： 姓名： 学号： 装 订 线
直流电机控制系统设计
直流电机控制系统设计
XX大学 课程设计 （论文） 题目直流电机控制系统设计 班级 学号 学生姓名 指导教师
沈阳航空航天大学 课程设计任务书 课程名称专业基础课程设计 院（系）自动化学院专业测控技术与仪器 班级学号姓名 课程设计题目直流电机控制系统设计 课程设计时间: 2012年7 月9 日至2012年7 月20 日 课程设计的内容及要求： 1.内容 利用51单片机开发板设计并制作一个直流电机控制系统。系统能够实时控制电机的正转、反转、启动、停止、加速、减速等。 2.要求 （1）掌握直流电机的工作原理及编程方法。 （2）掌握直流电机驱动电路的设计方法。 （3）制定设计方案，绘制系统工作框图，给出系统电路原理图。 （4）用汇编或C语言进行程序设计与调试。 （5）完成系统硬件电路的设计。 （6）撰写一篇7000字左右的课程设计报告。 指导教师年月日 负责教师年月日
学生签字年月日 目录 0 前言 (1) 1 总体方案设计 (2) 1.1 系统方案 (2) 1.2 系统构成 (2) 1.3 电路工作原理 (2) 1.4 方案选择 (3) 2 硬件电路设计 (3) 2.1 系统分析与硬件设计 (3) 2.2 单片机AT89C52 (3) 2.3 复位电路和时钟电路 (4) 2.4 直流电机驱动电路设计 (4) 2.5 键盘电路设计 (4) 3软件设计 (5) 3.1 应用软件的编制和调试 (5) 3.2 程序总体设计 (5) 3.3 仿真图形 (7) 4 调试分析 (9) 5 结论及进一步设想 (9) 参考文献 (10) 课设体会 (11) 附录1 电路原理图 (12) 附录2 程序清单 (13)
两相步进电机控制系统设计
综合课程设计 题目两相步进电机 学院计信学院 专业10自动化 班级2班 学生姓名 指导教师文远熔 2012 年12 月28 日
两相步进电机课程设计报告 步进电机是一种进行精确步进运动的机电执行元件，它广泛应用于工业机械的数字控制，为使系统的可靠性、通用性、可维护性以及性价比最优，根据控制系统功能要求及步进电机应用环境，确定了设计系统硬件和软件的功能划分，从而实现了基于8051单片机的四相步进电机的开环控制系统。控制系统通过单片机存储器、I/O 接口、中断、键盘、LED 显示器的扩展、步进电机的环形分频器、驱动及保护电路、人机接口电路、中断系统及复位电路、单电压驱动电路等的设计，实现了四相步进电机的正反转，急停等功能。为实现单片机控制步进电机系统在数控机床上的应用，系统设计了两个外部中断，以实现步进电机在某段时间内的反复正反转功能，也即数控机床的刀架自动进给运动，随着单片机技术的不断发展，单片机在日用电子产品中的应用越来越广泛，自六十年代初期以来，步进电机的应用得到很大的提高。人们用它来驱动时钟和其他采用指针的仪器，打印机、绘图仪，磁盘光盘驱动器、各种自动控制阀、各种工具，还有机器人等机械装置。此外作为执行元件，步进电机是机电一体化的关键产品之一，被广泛应用在各种自动化控制系统中，随着微电子和计算机技术的发展，它的需要量与日俱增，在各个国民经济领域都有应用。步进电机是机电数字控制系统中常用的执行元件，由于其精度高、体积小、控制方便灵活，因此在智能仪表和位置控制中得到了广泛的应用，大规模集成电路的发展以及单片机技术的迅速普及，为设计功能强，价格低的步进电机控制驱动器提供了先进的技术和充足的资源。 关键字: 步进电机单片机
直流电机控制系统设计.
XX大学 课程设计 （论文） 题目直流电机控制系统设计 班级 学号 学生姓名
指导教师 航空航天大学 课程设计任务书 课程名称专业基础课程设计 院（系）自动化学院专业测控技术与仪器 班级学号 课程设计题目直流电机控制系统设计 课程设计时间: 2012年7月9日至2012年7月20日 课程设计的容及要求： 1.容 利用51单片机开发板设计并制作一个直流电机控制系统。系统能够实时控制电机的正转、反转、启动、停止、加速、减速等。 2.要求 （1）掌握直流电机的工作原理及编程方法。 （2）掌握直流电机驱动电路的设计方法。 （3）制定设计方案，绘制系统工作框图，给出系统电路原理图。 （4）用汇编或C语言进行程序设计与调试。 （5）完成系统硬件电路的设计。 （6）撰写一篇7000字左右的课程设计报告。
指导教师年月日 负责教师年月日 学生签字年月日 目录 0 前言1 1 总体方案设计2 1.1 系统方案2 1.2 系统构成2 1.3 电路工作原理2 1.4 方案选择3 2 硬件电路设计3 2.1 系统分析与硬件设计3 2.2 单片机AT89C523 2.3 复位电路和时钟电路4 2.4 直流电机驱动电路设计4 2.5 键盘电路设计4 3软件设计5 3.1 应用软件的编制和调试5 3.2 程序总体设计5
3.3 仿真图形7 4 调试分析9 5 结论及进一步设想9参考文献10 课设体会11 附录1 电路原理图12附录2 程序清单13
直流电机调速系统设计 XXXXX大学自动化学院 摘要：本篇论文介绍了基于单片机的直流电机PWN调速的基本办法，直流电机调速的相关知识以及PWM调速的基本原理和实现方法。重点介绍了基于MCS-51单片机的用软件产生PWM信号以及信号占空比调节的方法。对于直流电机速度控制系统的实现提供了一种有效的途径。 直流电动机具有优良的调速特性，调速平滑，方便，调速围广，过载能力大，能承受频繁的冲击负载，可实现频繁的无级快速起动、制动和反转；能满足生产过程中自动化系统各种不同的特殊运行要求。电动机调速系统采用微机实现自动控制，是电气传动发展的主要方向之一。采用微机控制后，整个调速系统体积小，结构简单、可靠性高、操作维护方便，电动机稳态运转时转速精度可达到较高水平，静动态各项指标均能较好地满足工业生产中高性能电气传动的要求。 关键词：单片机最小系统；PWM ；直流电机调速; 0 前言 电动机作为最主要的机电能量转换装置，其应用围已遍及国民经济的各个领域和人们的日常生活。无论是在工农业生产，交通运输，国防，航空航天，医疗卫生，商务和办公设备中，还是在日常生活的家用电器和消费电子产品（如电冰箱，空调，DVD等）中，都大量使用着各种各样的电动机。据资料显示，在所有动力资源中，百分之九十以上来自电动机。同样，我国生产的电能中有百分之六十是用于电动机的。电动机与人的生活息息相关，密不可分。电气时代，电动机的调速控制一般采用模拟法，对电动机的简单控制应用比较多。简单控制是指对电动机进行启动，制动，正反转控制和顺序控制。然而近年来，随着技术的发展和进步，以及市场对产品功能和性能的要求不断提高，直流电动机的应用更加广泛，尤其是在智能机器人中的应用。直流电动机的起动和调速性能、过载能力强等特点显得十分重要，为了能够适应发展的要求，单闭环直流电动机的调速控制系统得到了很大的发展。而作为单片嵌入式系统的核心—单片机，正朝着多功能、多选择、高速度、低功耗、低价格、大存储容量和强I/O功能等方向发展。随着计算机档次的不断提高，功能的不断完善，单片机已越来越广泛地应用在各种领域的控制、自动化、智能化等方面，特别是在直流电动机的调速控制系统中。这是因为单片机具有很多优点：体积小，功能全，抗干扰能力强，可靠性高，结构合理，指令丰富，控制功能强，造价低等。所以选用单片机作为控制系统的核心以提高整个系统的可靠性和可行性。
无刷直流电机控制系统的设计
1引言无刷直流电机最本质的特征是没有机械换向器和电刷所构成的机械接触式换向机构。现在，无刷直流电机定义有俩种：一种是方波/梯形波直流电机才可以被称为无刷直流电机，而正弦波直流电机则被认为是永磁同步电机。另一种是方波/梯形波直流电机和正弦波直流电机都是无刷直流电机。国际电器制造业协会在1987年将无刷直流电机定义为“一种转子为永磁体，带转子位置信号，通过电子换相控制的自同步旋转电机”，其换相电路可以是独立的或集成于电机本体上的。本次设计采用第一种定义，把具有方波/梯形波无刷直流电机称为无刷直流电机。从20世纪90年代开始，由于人们生活水平的不断提高和现代化生产、办公自动化的发展，家用电器、工业机器人等设备都向着高效率化、小型化及高智能化发展，电机作为设备的重要组成部分，必须具有精度高、速度快、效率高等优点，因此无刷直流电机的应用也发展迅速[1]。 1.1 无刷直流电机的发展概况 无刷直流电动机是由有刷直流电动机的基础上发展过来的。 19世纪40年代，第一台直流电动机研制成功，经过70多年不断的发展，直流电机进入成熟阶段，并且运用广泛。 1955年，美国的D.Harrison申请了用晶体管换相线路代替有刷直流电动机的机械电刷的专利，形成了现代无刷直流电动机的雏形。 在20世纪60年代初，霍尔元件等位置传感器和电子换向线路的发现，标志着真正的无刷直流电机的出现。 20世纪70年代初，德国人Blaschke提出矢量控制理论，无刷直流电机的性能控制水平得到进一步的提高，极大地推动了电机在高性能领域的应用。 1987年，在北京举办的德国金属加工设备展览会上，西门子和博世两公司展出了永磁自同步伺服系统和驱动器，引起了我国有关学者的注意，自此我国开始了研制和开发电机控制系统和驱动的热潮。目前，我国无刷直流电机的系列产品越来越多，形成了生产规模。 无刷直流电动机的发展主要取决于电子电力技术的发展，无刷直流电机发展的初期，由于大功率开关器件的发展处于初级阶段，性能差，价格贵，而且受永磁材料和驱动控制技术的约束，这让无刷直流电动机问世以后的很长一段时间内，都停
基于单片机的步进电机控制系统的设计_毕业设计
本科毕业设计 基于单片机的步进电机控制系统的设计
摘要 随着自动控制系统的发展和对高精度控制的要求，步进电机在自动化控制中扮演着越来越重要的角色，区别于普通的直流电机和交流电机，步进电机可以对旋转角度和转动速度进行高精度控制。步进电机作为控制执行元件，是机电一体化的关键组成之一，广泛应用在各种自动化控制系统和精密机械等领域。 步进电机是将电脉冲信号转变为角位移或线位移的开环控制元件。在非超载的情况下，电机的转速、停止的位置只取决于脉冲信号的频率和脉冲数，而不受负载变化的影响，即给电机加一个脉冲信号，电机则转过一个步距角。 本系统介绍了一种基于单片机的步进电机控制系统的设计，包括了硬件设计和软件设计两部分。其中，硬件设计包括单片机最小系统、键盘控制模块、LCD显示模块、步进电机驱动模块、位置检测模块共5个功能模块的设计。系统软件设计采用C语言编写，包括主程序、数字键处理程序、功能键处理程序、电机驱动处理程序、显示模块、位置采集模块。 本设计采用STC89C52单片机作为主控制器，4*4矩阵键盘作为输入，LCD1602液晶作为显示，ULN2003A芯片驱动步进电机。系统具有良好的操作界面，键盘输入步进电机的运行距离；步进电机能以不同的速度运行，可以在不超过最大转速内准确运行到任意设定的位置，可调性较强；显示设定的运行距离和实际运行距离；方便操作者使用。关键词：单片机步进电机液晶显示键盘驱动
Design of the Stepping Motor Control System Based on SCM Qiu Haizhao (College of Engineering, South China Agricultural University, Guangzhou 510642,China) Abstract:With the development of automatic control system and the requirements of high-precision control, stepping motor control in automation is playing an increasingly important role, different from the common DC and AC motor, stepper motor rotation angle and rotational speed can be high-precision controlled. Stepper motor as a control actuator is a key component of mechanical and electrical integration, widely used in a variety of automated control systems and precision machinery and other fields. Stepper motor is the open-loop control components changing electric pulse signals into angular displacement or linear displacement .In the case of non-overloaded, the motor speed, stop position depends only on the pulse frequency and pulse number, regardless of load changes, that is, to add a pulse motor, the motor is turned a step angle. This system introduces a design of stepper motor control system based on single chip microcomputer, including hardware design and software design in two parts. Among them, the hardware design, including single chip minimal system, keyboard control module, LCD display module, the stepper motor drive module, position detection module five functional modules. System software design using C language, including the main program, process number keys, the key of function processes, motor driver handler, the display module, position acquisition module. This design uses STC89C52 microcontroller as the main controller, 4 * 4 matrix keyboard as an input, LCD1602 LCD as a display, ULN2003A chip as stepper motor driver. System has a good user interface, keyboard input stepper motor running distance; Stepper motor can run at different speed, and run to any given position accurately in any speed without exceeding the maximum speed, with a strong adjustable ; Display the running distance and the actual running distance, which is more convenient for the operator to use. Key words: SCM stepper LCD keyboard driver
直流电机控制系统设计范本
直流电机控制系统 设计
XX大学 课程设计 （论文）题目直流电机控制系统设计 班级 学号 学生姓名 指导教师
沈阳航空航天大学 课程设计任务书 课程名称专业基础课程设计 院（系）自动化学院专业测控技术与仪器 班级学号姓名 课程设计题目直流电机控制系统设计 课程设计时间: 7 月 9 日至 7 月 20 日 课程设计的内容及要求： 1.内容 利用51单片机开发板设计并制作一个直流电机控制系统。系统能够实时控制电机的正转、反转、启动、停止、加速、减速等。 2.要求 （1）掌握直流电机的工作原理及编程方法。 （2）掌握直流电机驱动电路的设计方法。 （3）制定设计方案，绘制系统工作框图，给出系统电路原理图。 （4）用汇编或C语言进行程序设计与调试。 （5）完成系统硬件电路的设计。 （6）撰写一篇7000字左右的课程设计报告。
指导教师年月日 负责教师年月日 学生签字年月日 目录 0 前言...................................................................................... 错误!未定义书签。 1 总体方案设计 ...................................................................... 错误!未定义书签。 1.1 系统方案 ...................................................................... 错误!未定义书签。 1.2 系统构成 ...................................................................... 错误!未定义书签。 1.3 电路工作原理............................................................... 错误!未定义书签。 1.4 方案选择 ...................................................................... 错误!未定义书签。 2 硬件电路设计 ...................................................................... 错误!未定义书签。 2.1 系统分析与硬件设计................................................... 错误!未定义书签。 2.2 单片机AT89C52............................................................ 错误!未定义书签。 2.3 复位电路和时钟电路................................................... 错误!未定义书签。 2.4 直流电机驱动电路设计 ............................................... 错误!未定义书签。 2.5 键盘电路设计............................................................... 错误!未定义书签。 3 软件设计 ............................................................................ 错误!未定义书签。 3.1 应用软件的编制和调试 ............................................... 错误!未定义书签。 3.2 程序总体设计............................................................... 错误!未定义书签。 3.3 仿真图形 ...................................................................... 错误!未定义书签。 4 调试分析 .............................................................................. 错误!未定义书签。
步进电机控制系统设计.
毕业设计论文 论文题目：基于单片机的步进电机控制电路板设计 摘要 随着微电子和计算机技术的发展，步进电机的需求量与日俱增，它广泛用于打印机、电动玩具等消费类产品以及数控机床、工业机器人、医疗器械等机电产品中，其在各个国民经济领域都有应用。研究步进电机的控制系统，对提高控制精度和响应速度、节约能源等都具有重要意义。 步进电机是一种能将电脉冲信号转换成角位移或线位移的机电元件，步进电机控制系统主要由步进控制器,功率放大器及步进电机等组成。采用单片机控制,用软件代替上述步进控制器,使得线路简单,成本低,可靠性大大增加。软件编程可灵活产生不同类型步进电机励磁序列来控制各种步进电机的运行方式。 本设计是采用AT89C51单片机对步进电机的控制，通过IO口输出的时序方波作为步进电机的控制信号，信号经过芯片ULN2003驱动步进电机；同时，用 4个按键来对电机的状态进行控制，并用数码管动态显示电机的转速。 系统由硬件设计和软件设计两部分组成。其中，硬件设计包括AT89C51单片机的最小系统、电源模块、键盘控制模块、步进电机驱动（集成达林顿ULN2003）模块、数码显示（SM420361K数码管）模块、测速模块（含霍尔片UGN3020）6个功能模块的设计，以及各模块在电路板上的有机结合而实现。软件设计包括键盘控制、步进电机脉冲、数码管动态显示以及转速信号采集模块的控制程序，最终实现对步进电机转动方向及转动速度的控制，并将步进电机的转动速度动态显示在LED数码管上，对速度进行实时监控显示。软件采用在Keil软件环境下编辑
************* 第1章绪论 1.1 课题背景 当今社会，电动机在工农业生产、人们日常生活中起着十分重要的作用。步进电机是最常见的一种控制电机，在各领域中得到广泛应用。步进电机作为执行元件，是机电一体化的关键产品之一, 广泛应用在各种自动化控制系统中。 随着微电子和计算机技术的发展，步进电机的需求量与日俱增，在各个国民经济领域都有应用。步进电机是一种将电脉冲转化为角位移的执行机构。当步进驱动器接收到一个脉冲信号，它就驱动步进电机按设定的方向转动一个固定的角度(称为“步距角”)，它的旋转是以固定的角度一步一步运行的。可以通过控制脉冲个数来控制角位移量，从而达到准确定位的目的；同时可以通过控制脉冲频率来控制电机转动的速度和加速度，从而达到调速的目的。步进电机可以作为一种控制用的特种电机，其优点是结构简单、运行可靠、控制方便。尤其是步距值不受电压、温度的变化的影响、误差不会长期积累的特点，给实际的应用带来了很大的方便。它广泛用于消费类产品（打印机、照相机、雕刻机）、工业控制（数控机床、工业机器人）、医疗器械等机电产品中。研究步进电机的控制和测量方法，对提高控制精度和响应速度、节约能源等都具有重要意义。控制核心采用C51芯片，它以其独特的低成本，小体积广受欢迎，当然其易编程也是不可多得的优点为此，本文设计了一个单片机控制步进电机的控制系统，可以实现对步进电机转动速度和转动方向的高效控制。 1.2 设计目的及系统功能 本设计的目的是以单片机为核心设计出一个单片机控制步进电机的控制系统。本系统采用AT89C51作为控制单元，通过键盘实现对步进电机转动方向及转动速度的控制，并且将步进电机的转动速度动态显示在LED数码管上。 1
毕业设计论文 基于单片机的步进电机控制器
第1章绪论 (2) 1.1引言 (2) 1.2步进电机常见的控制方案与驱动技术简介 (4) 1.2.1常见的步进电机控制方案 (4) 1.2.2步进电机驱动技术 (6) 1.3本文研究的内容 (8) 第2章步进电机概述 (9) 2.1步进电机的分类 (9) 2.2步进电机的工作原理 (10) 2.2.1结构及基本原理 (10) 2.2.2两相电机的步进顺序 (10) 2.3 步进电机的工作特点 (13) 2.4本章小结 (15) 第3章系统的硬件设计 (16) 3.1系统设计方案 (16) 3.1.1系统的方案简述与设计要求 (16) 3.1.2系统的组成及其对应功能简述 (16) 3.2单片机最小系统 (18) 3.2.1AT89S51简介 (18) 3.2.2单片机最小系统设计 (23) 3.2.3单片机端口分配及功能 (24) 3.3串口通信模块 (24) 3.4数码管显示电路设计 (25) 3.4.1共阳数码管简介 (25) 3.4.2共阳数码管电路图 (26) 3.5电机驱动模块设计 (27) 3.5.1L298简介 (27) 3.5.2电机驱动电路设计 (28) 3.6驱动电流检测模块设计 (30) 3.6.1OP07芯片简介 (30) 3.6.2ADC0804芯片简介 (32) 3.6.3电流检测模块电路图 (35) 3.7独立按键电路设计 (36) 3.8本章小结 (36) 第4章系统的软件实现 (37) 4.1系统软件主流程图 (37) 4.2系统初始化流程图 (38) 4.3按键子程序 (39) 结论 (43) 1
第1章绪论 1.1引言 步进电动机又称脉冲电动机或阶跃电动机，国外一般称为Steppingmotor、Pulse motor或Stepper servo，其应用发展已有约80年的历史。步进电机是一种把电脉冲信号变成直线位移或角位移的控制电机，其位移速度与脉冲频率成正比，位移量与脉冲数成正比。步进电机在结构上也是由定子和转子组成，可以对旋转角度和转动速度进行高精度控制。当电流流过定子绕组时，定子绕组产生一矢量磁场，该矢量场会带动转子旋转一角度，使得转子的一对磁极磁场方向与定子的磁场方向一着该磁场旋转一个角度。因此，控制电机转子旋转实际上就是以一定的规律控制定子绕组的电流来产生旋转的磁场。每来一个脉冲电压，转子就旋转一个步距角，称为一步。根据电压脉冲的分配方式，步进电机各相绕组的电流轮流切换，在供给连续脉冲时，就能一步一步地连续转动，从而使电机旋转。步进电机每转一周的步数相同，在不丢步的情况下运行，其步距误差不会长期积累。在非超载的情况下，电机的转速、停止的位置只取决于脉冲信号的频率和脉冲数，而不受负载变化的影响，同时步进电机只有周期性的误差而无累积误差，精度高，步进电动机可以在宽广的频率范围内通过改变脉冲频率来实现调速、快速起停、正反转控制等，这是步进电动机最突出的优点[1]。 正是由于步进电机具有突出的优点，所以成了机电一体化的关键产品之一，广泛应用在各种自动化控制系统中。随着微电子和计算机技术的发展，步进电机的需求量与日俱增，在各个国民经济领域都有应用[2]。比如在数控系统中就得到广泛的应用。目前世界各国都在大力发展数控技术，我国的数控系统也取得了很大的发展，我国已经能够自行研制开发适合我国数控机床发展需要的各种档次的数控系统。虽然与发达国家相比，我们我国的数控技术方面整体发展水平还比较低，但已经在我国占有非常重要的地位，并起了 2
直流电机控制系统设计(1)
湖南工程学院课程设计《DSP原理及应用》 题目：直流电机控制系统设计 专业： 班级： 姓名： 学号： 指导教师： 2015年5 月19 日
摘要 直流电动机具有优良的调速特性，调速平滑，方便，调速范围广，过载能力大，能承受频繁的冲击负载，可实现频繁的无级快速起动、制动和反转；能满足生产过程中自动化系统各种不同的特殊运行要求。电动机调速系统采用微机实现自动控制，是电气传动发展的主要方向之一。采用微机控制后，整个调速系统体积小，结构简单、可靠性高、操作维护方便，电动机稳态运转时转速精度可达到较高水平，静动态各项指标均能较好地满足工业生产中高性能电气传动的要求。 本篇论文介绍了基于单片机的直流电机PWN调速的基本办法，直流电机调速的相关知识以及PWM调速的基本原理和实现方法。重点介绍了基于TMS320LF2407单片机的用软件产生PWM信号以及信号占空比调节的方法。对于直流电机速度控制系统的实现提供了一种有效的途径。 关键词：单片机最小系统；PWM ；直流电机调速，TMS320LF2407;
前言 电动机作为最主要的机电能量转换装置，其应用范围已遍及国民经济的各个领域和人们的日常生活。无论是在工农业生产，交通运输，国防，航空航天，医疗卫生，商务和办公设备中，还是在日常生活的家用电器和消费电子产品（如电冰箱，空调，DVD等）中，都大量使用着各种各样的电动机。据资料显示，在所有动力资源中，百分之九十以上来自电动机。同样，我国生产的电能中有百分之六十是用于电动机的。电动机与人的生活息息相关，密不可分。电气时代，电动机的调速控制一般采用模拟法，对电动机的简单控制应用比较多。简单控制是指对电动机进行启动，制动，正反转控制和顺序控制。然而近年来，随着技术的发展和进步，以及市场对产品功能和性能的要求不断提高，直流电动机的应用更加广泛，尤其是在智能机器人中的应用。直流电动机的起动和调速性能、过载能力强等特点显得十分重要，为了能够适应发展的要求，单闭环直流电动机的调速控制系统得到了很大的发展。而作为单片嵌入式系统的核心—单片机，正朝着多功能、多选择、高速度、低功耗、低价格、大存储容量和强I/O功能等方向发展。随着计算机档次的不断提高，功能的不断完善，单片机已越来越广泛地应用在各种领域的控制、自动化、智能化等方面，特别是在直流电动机的调速控制系统中。这是因为单片机具有很多优点：体积小，功能全，抗干扰能力强，可靠性高，结构合理，指令丰富，控制功能强，造价低等。所以选用单片机作为控制系统的核心以
步进电机控制系统课程设计
河北xxxxxx学院 课程设计说明 书 题目：步进电机控制系统 学院（系）： 年级专业： 学号： 学生姓名： 同组学生： 指导教师：
步进电机控制系统 设计者：xxxxx 指导老师：xxxx 1摘要： 由于步进电机自身的特点、不需要位置、速度等信号反馈，只需要脉冲发生器产生足够的脉冲数和合适的脉冲频率，就可以控制步进电机移动的距离和速度。步进电机的运转方向的控制为输入电机各绕组的通电顺序。例如，一个三相步进电机的通电顺序为：a—ab—b—bc—c—ca—a--.....，此时点击正转，若通电顺序改为：a—ac—c—cb—b—ba—a--.....时点击反转。既可以通过改变环形分配器的脉冲输出顺序，也可以通过编程改变输出脉冲的顺序，来改变输入到各绕组的通电顺序，达到控制电击方向的目的。 关键词：步进电机 PLC 步进电机驱动器 引言步进电机是一种常用的电气执行原件，一种多相或单相同步点击，在数控机床、包装机械等自动控制及检测仪表等方面得到广泛运用。随着plc的不短发展。其功能越来越强大，除了有简单的逻辑功能和顺序控制外，运算功能的加入、pid和各类高速指令、使得plc对复杂和特殊系统的控制应用更加广泛。Plc与数控技术的结合产生了各种不同类型的数控设备。 2 任务与要求 (1) 了解步进电机的原理 (2) 熟练使用PLC控制步进电机，了解步进电机驱动器原理 3 装置原理介绍 3.1控制系统功能框图 在步进电机控制系统中，首先控制步进电机使之稳步启动，然后高速运动，接近制定位置时，减速之后低速运动一段时间，在准确地停在预定的位置上，最后步进电机停留2s后，按照前进时的加速—高速—减速—低速的步骤返回到起始点，其运动状态转换过程平稳，其功能框图如图3.1所以，其简单工作过程如图3.2所示。 由于步进电机本身的结构特性决定了它要实现高速运转必须有加速过程，如果在启动时突然加载高频脉冲，电机会产生啸叫、失步甚至不能启动，在停止阶段也是这样，当高频脉冲突然降到零时，电机会产生啸叫和振动，所以在启动和停止时，都必须有一个加速和减速过程。 3.2步进电机控制系统硬件设计 由于步进电机的硬件结构特性，所以对输入的脉冲的频率有所限制，对于低频的脉冲输出时，plc可以利用定时器来完成。若要求步进电机的速度较快时，就需要用plc的高速脉冲输出指令，这时就需要在程序中设置相应的步骤来完成对步进电机的控制。 3.21 组建器材 （1）主机plc 根据系统的控制要求，采用三菱FX系统系列的plc作为控制器。（2）限位开关此系统中共用了两个限位开关：左限位开关和右限位开关。这两个限位开关的作用是控制物体的位置，防止物体超出合理的工作范围。 （3）步进电机步进电机是该系统的执行机构
直流电动机控制系统设计
X X X X X学院 题目：直流电动机控制系统 学 院 XXXXXX学院 专 业 自动化 班 级 XX班 姓 名 XXX 学 号 XXXXX 指导老师 XXX 2012年 12 月 25 日 1、 设计题目：直流电动机控制系统 1、前言 近年来，随着科技的进步，电力电子技术得到了迅速的发展，直流电机得到了越来越广泛的应用。直流它具有优良的调速特性,调速平滑、方便,调速范围广;过载能力大,能承受频繁的冲击负载,可实现频繁的无级快速起动、制动和反转;需要能满足生产过程自动化系统各种不同的特殊运行要求，从而对直流电机的调速提出了较高的要求，改变电枢回路电阻调速，改变电枢电压调速等技术已远远不能满足要求，这时通过PWM方式控制直流电机调速的方法应运而生。 采用传统的调速系统主要有以下缺陷：模拟电路容易随时间漂移，会产生一些不必要的热损耗，以及对噪声敏感等。而在用了PWM技术后，避免了以上的缺陷，实现了用数字方式来控制模拟信号，可以大幅度降低成本和功耗。另外，由于PWM 调速系统的开关频率较高,仅靠电枢电感的滤波作用就可获得平稳的直流电流,低速特性好；同样,由于开
关频率高,快速响应特性好,动态抗干扰能力强,可以获得很宽的频带;开关器件只工作在开关状态,主电路损耗小,装置效率高。PWM 具有很强的抗噪性，且有节约空间、比较经济等特点。 2、系统设计原理 脉宽调制技术是利用数字输出对模拟电路进行控制的一种有效技术，尤其是在对电机的转速控制方面，可大大节省能量，PWM控制技术的理论基础为：冲量相等而形状不同的窄脉冲加在具有惯性的环节上时，其效果基本相同，使输出端得到一系列幅值相等而宽度不相等的脉冲，用这些脉冲来代替正弦波或其他所需 要的波形。按一定的规则对各脉冲的宽度进行调制，既可改变逆变电路输出电压的大小，也可改变输出频率。 直流电动机的转速n和其他参量的关系可表示为 （1） 式中 Ua——电枢供电电压（V）； Ia ——电枢电流（A）； Ф——励磁磁通（Wb）； Ra——电枢回路总电阻（Ω）； CE——电势系数， ，p为电磁对数，a为电枢并联支路数，N为导体数。 由式（1）可以看出，式中Ua、Ra、Ф三个参量都可以成为变量，只要改变其中一个参量，就可以改变电动机的转速，所以直流电动机有三种基本调速方法：(1)改变电枢回路总电阻Ra；；(2)改变电枢供电电压Ua；(3)改变励磁磁通Ф。 3、方案选择及论证 3.1、方案选择 3.1.1、改变电枢回路电阻调速 可以通过改变电枢回路电阻来调速，此时转速特性公式为 n=U-【I(R+Rw)】/KeФ （2）式中Rw为电枢回路中的外接电阻（Ω）。 当负载一定时，随着串入的外接电阻Rw的增大，电枢回路总电阻R= (Ra+Rw)增大，电动机转速就降低。Rw的改变可用接触器或主令开关切换来实现。 这种调速方法为有级调速，转速变化率大，轻载下很难得到低速，
步进电机控制系统设计
课程设计任务书 设计题目：微机步进电机控制系统设计 设计目的： 1.巩固和加深课堂所学知识； 2.学习掌握一般的软硬件的设计方法和查阅、运用资料的能力； 3.通过步进电机控制系统设计与制作，深入了解与掌握步进电机的运行方式、方向、速 度、启/停的控制。 设计任务及要求：（在规定的时间内完成下列任务） 任务：控制四相步进电机按双八拍的运行方式运行。按下开关SW1时启动步进电机，按ESC键停止工作。采用循环查表法，用软件来实现脉冲循环分配器的功能 对步进电机绕组轮流加电。 要求对题目进行功能分析（四项功能：快速顺时针旋转，慢速顺时针旋转， 快速逆时针旋转和慢速逆时针旋转），进行步进电机远程控制系统硬件电路设 计，画出电路原理图、元器件布线图、实验电路图；绘制程序流程图，进行 步进电机控制程序设计（采用8086汇编语言）；系统调试、运行，提交一个 满足上述要求的步进电机控制系统设计。 时间安排：(部分时间,某些工作可以自己安排重叠进行) 具体要求：设计报告撰写格式要求（按提供的设计报告统一格式撰写）， 具体内容如下： ①设计任务与要求②总体方案与说明 ③硬件原理图与说明④实验电路图与说明 ⑤软件主要模块流程图 ⑥源程序清单与注释 ⑦问题分析与解决方案（包括调式记录、调式报告，即在调式过程中遇到的主要问 题、解决方法及改进设想）； ⑧小结与体会 附录：①源程序（必须有简单注释）②使用说明③参考资料 指导教师签名：08 年12 月01 日 教研室主任（或责任教师）签名：年月日
目录 第1章需求分析 (1) 1.1课程设计题目 (1) 1.2步进电机介绍 (1) 1.3课程设计任务及要求 (1) 1.4软硬件运行环境及开发工具 (1) 第2章概要设计 (2) 2.1设计原理及实现方法 (2) 2.1.1 步进电机控制原理 (2) 2.1.2微机步进电机控制系统原理图 (2) 2.1.3 运行方式与方向的控制——循环查表法 (3) 2.1.4步进电机的启/停控制——设置开关 (4) 2.2微机步进电机控制系统设计流程图 (4) 第3章详细设计 (5) 3.1 硬件设计与实现 (5) 3.2软件设计 (5) 3.2.1正向慢转子程序 (5) 3.2.2正向快转子程序 (6) 3.2.3反向慢转子程序 (6) 3.2.4反向快转子程序 (6) 3.2.5长延时子程序 (7) 3.2.6短延时子程序 (7) 第4章系统调试与操作说明 (7) 4.1系统调试 (7) 4.2 操作说明 (8) 第5章课程设计总结与体会 (8) 参考文献 (9) 附录微机步进电机控制系统源程序 (9)