电动舵机模糊自适应pid控制研究Fuzzy adaptive PID control of electric actuator.docx

1、电动舵机模糊自适应pid控制研究Fuzzy adaptive PID control of electric actuator电动舵机模糊自适应pid控制研究（Fuzzy adaptive PID control of electric actuator）Fuzzy adaptive PID control of electric actuatorCao Jing Zhu Jihong 1 1, 21 、 Department of computer science and technology, Tsinghua University, Beijing 1000842, Jiangsu Co

2、llege of Information Technology, Jiangsu, Wuxi 214106CaojingAbstract: This paper presents a fuzzy adaptive PID controller, this method for electromechanical actuator position servo system, overcomes some shortcomings of the simple fuzzy control and traditional PID control, reasoning and decision-mak

3、ing through fuzzy rules, real-time optimization of PID controller parameters, the simulation and experimental results show that this method improves the control performance the system has a good control effect. Keywords fuzzy control; PID control; electric actuator; MATLAB;Study, of, Fuzzy, self-tur

4、ning, PID, control, of, electromechanical, actuatorCaojing1, 2, zhujihong1,1, Department, of, Computer, Science, and, Technology, Tsinghua, University, Beijing 1000842 Jiangsu College of Information Technology Wuxi Jiangsu text introduces a kind 210106 Abstract:The of self-turning Fuzzy-PID controll

5、er, it is used on the electromechanical actuator position servo system. It overcomes some defects of simply fuzzy control and traditional PID control, The real-time optimization of the control parameter of PID controller is carried out by fuzzy reasoning and strategy.The simulation and experiment re

6、sults show that this method improves the control performance of system and has preferable effects. Key words:Fuzzy control PID control electromechanical actuator (EMA) MATLAB1 IntroductionThe electric actuator (EMA) has the advantages of simple structure, light weight, load characteristic and high r

7、eliability, thus in drones (UAV), has been widely used in aircraft and missile and spacecraft.The traditional PID control with the characteristics of real-time, easy realization, is widely applied in the control system, as long as the correct parameters, the PID controller can realize its function,

8、but the autopilot system exists nonlinear, time-varying and uncertain factors, the control effect of PID will be difficult to achieve the desired objectives. The fuzzy control has strong adaptability to the nonlinear and time-varying of the control object, and its flexibility and robustness are bett

9、er, and the control is simple, and it is widely used in the field of motor control. But in the fuzzy control system, it is difficult to completely eliminate the steady-state error. In general, the control accuracy is not ideal.According to the characteristics of the two kinds of controller, in order

10、 to improve the control performance of the actuator position servo system, this paper designed a fuzzy adaptive PID controller, taking into account the advantages of the two kinds of control methods, reasoning and decision-making by fuzzy rules, three parameters on-line tuning of PID controller, the

11、 experimental results show that the controller has a simple structure. The effect is good.2 the mathematical model of the electric actuatorThe main component of electric actuator position servo system of rare earth permanent magnet brushless DC motor, because of the air gap magnetic field, back EMF

12、and current is non sinusoidal, the direct use of phase variables to establish the mathematical model of the motor itself. Take the two-phase conduction star type three-phase six state as an example, in order to facilitate the analysis, assume the motor:(1)The magnetic circuit is not saturated, ignor

13、ing the influence of hysteresis and eddy current loss, ignoring the influence of higher spatial potential harmonic;(2)The three-phase winding is symmetrical, the air gap magnetic field is square wave, and the stator current and rotor magnetic field distribution are symmetrical;(3)The armature windin

14、gs are distributed continuously and evenly on the inner surface of the stator;(4)The effects of cogging, commutation and armature reaction are neglected;(5)There is no damping winding on the rotor, and the permanent magnet can not play a damping role. According to the voltage balance relationship of

15、 the three-phase winding circuit, the following differential equations can be listed 1:RI IL M MM L MM M L00.UEAS a a a. =. +.P. +.RI IZeroZero(1)UEBB B BSRI I00UECS c c cType: UA, UB, UC is stator winding voltage; IA, IB, IC are stator winding current; EA, eb,EC is the stator winding electromotive

16、force; the Rs represents the motor winding resistance, and the L is self inductance of each phase winding; the M is between each phase windingMutual inductance; P is a differential operator, P=D / dt.The three phase winding is star connected and has no midlineIIB+Ic = 0 (2)+AMiMi =.Mi (3)+Therefore,

17、 availableBa CBy means of (1), (2) and (3) formulas, the voltage equation isIRLMI I00.00.U uEA sA a a.=.+.+.RILMZeroZeroZero.Zero(4)P eBB B BSRILMI0000.UECS c c cBecause there are only two phases at any time, there isD (II IB) U.ADtR (IA.IB)L.M.U()(.)+UE e=B BA s a.Due to the symmetry of three-phase

18、 winding, there are: uU, u, u, u, u=D, B, Ba, C, C, aII, I, I, I, I.=D, B, Ba, C, C, a.EE, e, e, e, e, e=D, B, Ba, C, C, aLdLM.=Omega0DDT NOmegaDOmegaKTKmi, TmJTl+And because of E?=,D DMWhere UD represents the voltage applied to the motor phase winding, ID represents the turn-on current phase windin

19、g, Km torque constant, Ke EMF constant, J is converted to the motor shaft of the total moment of inertia, load torque for Tl, N for the reduction ratio of the reduction mechanism.After finishing the Laplace transformation, the transfer function of whole motor system (ignoring load torque) for:EDT(=

20、=)Gs, U, theta (SS), NK, s (Ts, 1, +1) (Ts, +1) (5)Mm LAmong them, Tm, =Rs, J is the electromechanical time constant of the motor system; Tl =Ld is the armature loop electromagnetic time constant. KK REM s3 fuzzy adaptive PID control system design3.1 control system structure and working principleThe

21、 structure of the fuzzy adaptive PID control system is shown in Figure 1. It is composed of a traditional PID controller and a fuzzy control unit. The displacement sensor to detect the position of the system, the signal conversion and computer serial communication, R given signal and the feedback si

22、gnal Y the deviation between the input signal e, the deviation signal is divided into two, all the way directly into the PID controller, the other way and its rate of change in fuzzy control, get the parameters of.Kp,.Ki,.Kd correction, automatic correction of PID parameters Kp, Ki, Kd initial, and

23、then use the input parameters of PID controller after correction under control, as the given speed loop. The system adopts double closed loop control, inner loop is speed loop, PI control is adopted, outer loop is position loop, and fuzzy PID control is adopted.Fuzzy control linkParameter correction

24、KpKiKdRUPID controllerYcontrol objectFig. 1 block diagram of fuzzy self-tuning PID control systemThree point twoFuzzy PID controller designThree2.1 establish input and output variables and paste themThe input variables of the fuzzy controller are the deviation of the steering angle of the steering g

25、ear, e and the deviation change rate EC, the output variable is PID, and the control correction parameters are.Kp,.Ki and.Kd. Whether it is the deviation or the rate of change, it is a precise input value, and fuzzification is to make it discrete and change into an element in the set of integer argu

26、ments. Lets define E and EC as follows:E =k1(k). Theta theta (K.1) ec=k2e (k).E (K.1) type: K1, K2 angle deviation and rate of change scale transform scale factor, the actual range of E and EC were .em, EM, .ecm, ECM, the fuzzy variable scale transform and quantization after respectively as E, EC, f

27、orThe control precision of servo system, the E and EC domain is defined as E, EC=-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6 of the fuzzy subset is: E, EC=NB, NM, NS, O, PS, PM, PB, sub the concentration of elements representing the negative, negative, negative, zero, is small, middle, big, fuzzy me

28、mbership function with triangular function. The outputs of.Kp,.Ki,.Kd, domain, language variables and membership functions are the same as those of E and EC.3.2.2 establishes fuzzy inference rulesThe design of fuzzy control is the core technology of the knowledge of the expert or control engineer lo

29、ng-term accumulation and practical experience based on the fuzzy inference rules of the system stability and response speed, the overshoot and steady-state accuracy summed up. Its general form is a fuzzy conditional statement consisting of fuzzy language and fuzzy logic. This system uses Mamdani fuz

30、zy inference type, that is, fuzzy implication relation is:If, E, =Ai, and, EC, =Bj, then,.Kp = Cij, and,.Ki = Dij, and,.Kd = FijThe basic reasoning principle of fuzzy controller is: when the deviationEFor large, makes the system have good tracking performance, Kp should take a larger; and in order t

31、o avoid differential overflow, Kd should be small, at the same time to avoid the overshoot of the system, deal with the integral role restrictions, Ki usually zero when the deviation;EFor medium hours, in order to reduce the overshoot of the system, Kp should be reduced; at this time, the value of Kd has a greater impact on the system, and t