CN102420553A - Multi-motor proportional synchronization control algorithm based on improved adjacent cross coupling - Google Patents
Multi-motor proportional synchronization control algorithm based on improved adjacent cross coupling Download PDFInfo
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Abstract
The invention discloses a multi-motor proportional synchronization control algorithm based on improved adjacent cross coupling. By the algorithm, an adjacent cross-coupling control structure is improved, so that each motor in a multi-motor synchronization system can be synchronously run at proportional rotating speed. Simultaneously, for the characteristics of time varying, nonlinearity and the like of the motor, fuzzy proportion integration differentiation (PID) controllers are designed, and an improved adjacent cross-coupling multi-motor proportional synchronization fuzzy PID control algorithm is provided. Simulation comparison between the control algorithm and the conventional PID algorithm proves that the control algorithm has high convergence speed, high stability, high dynamic performance and practical value and capability of well realizing the proportional synchronous running of a plurality of motors.
Description
Technical field
The invention belongs to many motor synchronous control technology, particularly a kind of many motors ratio Synchronization Control based on intelligent control algorithm.
Background technology
In systems such as weaving, papermaking, film take-up, the wide range of problem of many motor synchronous controls exists.Common many motor synchronous relations are ω 1=ω 2=...=ω n, promptly the speed synchronization proportionality coefficient of each motor all is 1, it is the simplest synchronized relation.But when actual production such as film stretching rolling, for guaranteeing that film moves with certain force of strain, the speed between the motor need increase progressively by a certain percentage, and promptly ω 1: ω 2: ...: ω n=μ 1: μ 2: ...: μ n.The quality of many motor synchronous controls directly influences the reliability and the control precision of system.
Traditional Synchronization Control structure mainly comprises parallel control, principal and subordinate's control, and virtual line shaft control etc., control accuracy is not high.For improving control precision; Koren proposes parallelly connected cross coupling structure, as motor number n>2 the time, because of the compensation rule very difficult definite and inapplicable (1.Yoram Koren. Cross-coupled biaxial computer controls for manufacturing systems [J]. Journal of Dynamic Systems; Measurement and Control; 1980,102 (4), 265-272).Perez-Pinal has proposed to be applicable to motor number n>2 inclined to one side cross-coupling control, it can realize net synchronization capability well, but when the motor number is too much; Control structure very complicated (2.Perez-Pinal, F.J., Calderon; G.; Araujo-Vargas, I. Relative Coupling Strategy [C] // IEEE International Electric Machines and Drives Conference, 2003:1162-1166).People such as Shih had proposed adjacent cross-coupling control structure on this basis in 2002, as far as every motor, only considered the influence of adjacent two motor speeds, inclined to one side relatively cross-couplings, and control structure is simplified widely.But because the dynamic characteristic of Alternating Current Governor System; Become when having inner parameter, factor such as non-linear and load variations; People are difficult to set up controlled process Mathematical Modeling accurately; The application of this strategy is restricted (3.Yi-Ti Shih, Chin-Sheng Chen, An-Chen Lee. A novel cross-coupling control design for Bi-axis motion [J]. International Journal of Machine Tool and Manufacture; 2002,42 (14): 1539-1548).People such as Cao Ling sesame had added sliding mode controller on adjacent cross-linked basis in 2008; It can reduce external disturbance and inner parameter changes the influence that brings, and obtains very high synchronous control accuracy (4.CAO L Z, LI C W; NIU C; Et al. Synchronized sliding-mode control for multi-induction motors based on adjacent cross-coupling [J]. Electric machines and control, 2008,12 (5): 586-592. 5.LI C W; ZHAO D Z; REN J. Total sliding mode speed synchronization control of multi induction motors [J]. Systems Engineering Theory and Practice, 2009,29 (10): 110-117).It is applicable to that proportionality coefficient is 1 many motor synchronous control, and when need be with certain proportion between many motors synchronous, this Algorithm design be comparatively complicated, in application, acquires a certain degree of difficulty.
Summary of the invention
The object of the present invention is to provide a kind of Synchronization Control algorithm, thereby realize the arbitrary proportion Synchronization Control of many motors based on adjacent cross-couplings of modified model and fuzzy controller.
The technical solution that realizes the object of the invention is: a kind ofly based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model adjacent cross-coupling control structure is improved; Many motor synchronous system are divided into n synchronization subsystem; Define the Control Parameter of each synchronization subsystem in the adjacent cross-coupling control structure; Increased proportional component; Control Parameter to each synchronization subsystem defines again again, and sets up the relation equation group of the Control Parameter of definition again, makes the synchronous operation in proportion of the rotating speed between each motor in many motor synchronous system; In each synchronization subsystem, comprise 3 parameter fuzzy from the Tuning PID Controller device, realize a tracking error controlled function and two synchronous error controlled function respectively.
The present invention compared with prior art, its remarkable advantage:
(1) adjacent cross-coupling control structure is improved, increased proportional component on the original basis, Control Parameter is defined again, and set up the relation equation group of the Control Parameter of definition again.This modified model control structure can not only realize that the speed of many motors is synchronous fully, can also realize that many motors carry out with arbitrary proportion synchronously.
(2) utilize the thought of fuzzy reasoning, fuzzy controller is combined with the PID controller, designed fuzzy controller.Compare with conventional PID controllers, it can overcome parameter time varying in the complication system, problem such as non-linear better, eliminates steady-state error, has higher synchronous control precision and convergence rate faster.
Description of drawings
Fig. 1 is the adjacent cross-coupling control structure of modified model.
Fig. 2 is the fuzzy controller structure.
Fig. 3 a, 3b are the simulated program of this control algolithm in Matlab software.
Fig. 4 a, 4b, 4c, 4d are the adjacent cross-couplings fuzzy control of modified model simulation curves.
Fig. 5 a, 5b, 5c, 5d are the adjacent cross-couplings PID control of modified model simulation curves.
Fig. 6 is the flow chart that the present invention is based on the Synchronization Control algorithm of adjacent cross-couplings of modified model and fuzzy controller.
Embodiment
The present invention is a kind of to be carried out having added fuzzy controller on the improved basis to adjacent cross-coupling control structure based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model, and step is following:
The first step: define the parameter in the adjacent cross-coupling control structure, the basic thought of adjacent cross-coupling control structure is, when each motor is implemented control, only considers the state of self and adjacent two motors, and this is simple control structure greatly; On this basis; Adjacent cross-coupling control structure is improved, many motor synchronous system are divided into n synchronization subsystem, define the Control Parameter of each synchronization subsystem in the adjacent cross-coupling control structure; Increased proportional component; Control Parameter to each synchronization subsystem defines again again, and sets up the relation equation group of the Control Parameter of definition again, makes the synchronous operation in proportion of the rotating speed between each motor in many motor synchronous system.
Second step:, have phenomenons such as parameter time varying, non-linear, delay during operation, and fuzzy control does not rely on the accurate model of object, has good self study and non-linear approximation capability because the dynamic characteristic of many motors has nothing in common with each other.Therefore, the present invention utilizes the thought of fuzzy reasoning, and fuzzy controller is combined with conventional PID controllers, in the adjacent cross-coupling control structure of modified model, designs parameter fuzzy from the Tuning PID Controller device.In each synchronization subsystem, comprise 3 parameter fuzzy from the Tuning PID Controller device, realize a tracking error controlled function and two synchronous error controlled function respectively.Therefore, comprise 3n fuzzy controller in the The whole control structure altogether.
Below in conjunction with accompanying drawing the present invention is described in further detail.
The present invention is based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model, adjacent cross-coupling control structure is being carried out having added fuzzy controller on the improved basis.Specific operation process is following:
The first step: combine Fig. 1, establishing synchronous motor has the n platform, should many motor synchronous system be divided into n synchronization subsystem, and division methods is: (1,2,3), (2,3; 4) ..., (i-1, i, i+1) ..., (n-1; N, 1), (n, 1,2), n >=3, i is any motor;
Second step: the tracking error that defines i platform motor is:
e
i(t)=ω
i(t)-ω
i*(t)
Wherein, ω
i(t) be that i platform motor is at t output speed constantly, ω
i* (t) is that i platform motor is in t reference velocity constantly, t>=0;
The 3rd step: the synchronous error ε that defines i platform motor and i-1 platform motor
I1(t) and the synchronous error ε of i platform motor and i+1 platform motor
I2(t) be respectively:
ε
i1(t)=e
i(t)-e
i-1(t)
ε
i2(t)=e
i(t)-e
i+1(t)
The 4th step: the Control Parameter relation equation group of setting up each synchronization subsystem in the adjacent cross-coupling control structure: the tracking error e that makes every motor
i(t), every motor is adjacent the synchronous error ε of two motors
I1(t), ε
I2(t) stable convergence, promptly to satisfy following formula:
This formula is applicable to the simplest synchronous proportional coefficient μ
i=1 synchronous control system.
The 5th step: when the rotation speed relation between the n platform motor in many motor synchronous system is ω
1: ω
2: ...: ω
n=μ
1: μ
2: ...: μ
nThe time, μ
iBe the proportionality coefficient of i platform motor, μ
i＞0, i ∈ n, the ratio synchronous error that defines between two motors is the difference of each motor after t output speed constantly is divided by proportionality coefficient separately, then the ratio synchronous error ε of i platform motor and i-1 platform motor
I1 *(t) and the ratio synchronous error ε of i platform motor and i+1 platform motor
I2 *(t) be respectively:
ε
i1 *(t)=ω
i(t)/μ
?i-ω
i-1(t)/μ
?i-1
ε
i2 *(t)=ω
i(t)/μ
?i-ω
i+1(t)/μ
?i+1
The 6th step: set up relation equation group: when each subsystem is controlled, should be able to make e through parameter in the adjacent cross-coupling control structure of definition again
i(t), ε
I1 *(t), ε
I2 *(t) converge on zero, promptly will satisfy following formula:
The 7th step: among Fig. 1, make ω * (t)=ω
i* (t)/μ
i, it is that i platform motor is at the proportionality coefficient of t reference velocity constantly divided by this motor; Unified reference speed value ω * (t) of given each motor of elder generation, then i platform motor is at t reference velocity ω constantly
i* (t) unifies reference speed value ω * (t) for this and multiply by proportionality coefficient μ separately
i, come the reference velocity ω of each motor of control in real time by fuzzy controller again
i* (t);
The 8th step: combine Fig. 1, Fig. 2, in the adjacent cross-coupling control structure of modified model, utilize the thought of fuzzy reasoning, fuzzy controller is combined with the PID controller, the design fuzzy controller.
In each synchronization subsystem, comprise 3 parameter fuzzy from the Tuning PID Controller device, realize a tracking error controlled function and two synchronous error controlled function respectively; What be used for realizing the tracking error controlled function is called the tracking error controller again, and what be used for realizing the synchronous error controlled function is called the synchronous error controller again; Definition C
I0Be the tracking error controller of i platform motor, C
I1, C
I2Be two synchronous error controllers of i platform motor; C
I0Deviation be input as e
i(t), be output as u
I0(t), C
I1, C
I2Deviation input be respectively ε
I1 *(t), ε
I2 *(t), output is respectively u
I1(t), u
I2(t), u then
i(t)=u
I0(t)+u
I1(t)+u
I2(t) be the output of i synchronization subsystem, i.e. the speed control amount of i platform motor.In the adjacent cross-coupling control structure of this modified model, comprise 3n fuzzy controller altogether.
The design procedure of fuzzy controller is following:
(1) combines Fig. 2; Fuzzy controller is divided into fuzzy controller and parameter can be adjusted PID controller two parts; Fuzzy controller adopts dual input three output control structures, and the deviation e of motor speed and deviation variation rate ec are input, and parameter can be adjusted three parameter Δ K of PID controller
P, Δ K
I, Δ K
DBe output; According to the variation of input e, ec, to parameter Δ K
P, Δ K
I, Δ K
DCarry out online self-tuning, obtain new PID controller parameter K
P, K
I, K
DThereby the PID controller is again to the change amount of motor output speed;
(2) to input e, ec and output Δ K
P, Δ K
I, Δ K
DCarry out obfuscation; Set respectively 7 linguistic variables for NB, NM, NS, Z, PS, PM, PB}, it is negative big that implication is followed successively by, negative in, negative little, zero, just little, the center, honest; Their basic domain, fuzzy domain and quantizing factor are distinguished as follows:
e：(-a,a)，{-6，6}，?K
e=6/a；
ec：(-b,b)，{-6，6}，K
ec=6/b；
ΔK
P：(-c,c)，{-6，6}，K
ΔKP=c/6；
ΔK
I：(-d,d)，{-6，6}，K
ΔKI=d/6；
ΔK
D：(-e,e)，{-6，6}，K
ΔKD=e/6；
Wherein, a, b, c, d, e are the constant greater than 0, K
e, K
Ec, K
Δ KP, K
Δ KI, K
Δ KDBe respectively e, ec, Δ K
P, Δ K
I, Δ K
DQuantizing factor;
(3) set up input e, ec and output Δ K
P, Δ K
I, Δ K
DMembership function, all get trigonometric function;
(4) formulate fuzzy control rule, be total to (7 * 7) 49, it is following to formulate principle:
When big, for accelerate response speed and when preventing to begin deviation e moment become greatly, get bigger K
PWith less K
DWhen
With
Be median size, for the overshoot that makes system responses reduces K
P, K
I, K
DAll can not be too big, should get less K
IValue, K
PAnd K
DSize is moderate, to guarantee the response speed of system; When
Less, have good steady-state behaviour for making system, should increase K
PAnd K
IValue, simultaneously for avoiding system oscillation, and consider anti-interference, K
DBe generally median size.
Fuzzy control rule is formulated to Δ K
P, Δ K
I, Δ K
DFuzzy control rule table, like table 1.
Table 1 Δ K
P, Δ K
IWith Δ K
DFuzzy reasoning table
(5) fuzzy control rule with fuzzy reasoning table generates following conditional statement:
If?e?is?Ai?and?ec?is?Bi,?then?ΔK
P?is?Ci,?ΔK
I?is?Di,?ΔK
D?is?Ei
Ai in the conditional statement, Bi, Ci, Di and Ei are the fuzzy sets of input/output variable, and then fuzzy reasoning table can be regarded as by the constructed rule base of a series of dual input three output rules, totally 49:
1.If?e?is?NB?and?ec?is?NB,?then?ΔK
P?is?PB,?ΔK
I?is?NB,?ΔK
D?is?PS
2.If?e?is?NB?and?ec?is?NM,?then?ΔK
P?is?PB,?ΔK
I?is?NB,?ΔK
D?is?NS
3.If?e?is?NB?and?ec?is?NS,?then?ΔK
P?is?PM,?ΔK
I?is?NM,?ΔK
D?is?NB
……
49.If?e?is?PB?and?ec?is?PB,?then?ΔK
P?is?NB,?ΔK
I?is?PB,?ΔK
D?is?PB
(6) according to fuzzy control rule, carry out fuzzy reasoning, fuzzy reasoning adopts " Mamdani " reasoning algorithm commonly used on the engineering, adopts " very big-minimum " composition rule to carry out fuzzy operation, obtains Δ K respectively
P, Δ K
I, Δ K
DThe degree of membership of all fuzzy value under different e and ec, follow the principles into:
μ
U(Z
U)?=?(w
1∧μ
C1(U))∨(w
2∧μ
C2(U))
w
1=μ
A1(E
0)∧μ
B1(EC
0)
w
2=μ
A2(E
0)∧μ
B2(EC
0)
Wherein, " ∧ " for getting little computing, " ∨ " is for getting macrooperation; μ
U(Z
U) be the degree of membership of controlled quentity controlled variable U fuzzy set, controlled quentity controlled variable U comprises Δ K
P, Δ K
I, Δ K
DE
0Be error; EC
0For error changes; μ
A1(E
0), μ
A2(E
0) be E
0To fuzzy set A
1, A
2Degree of membership; μ
B1(EC
0), μ
B2(EC
0) be EC
0To fuzzy set B
1, B
2Degree of membership; μ
C1(U), μ
C2(U) be that controlled quentity controlled variable U is to fuzzy set C
1, C
2Degree of membership.
(7) by e, ec, Δ K
P, Δ K
I, Δ K
DFuzzy subset's degree of membership, and the measured value of error e and error rate ec uses reverse gelatinization gravity model appoach to calculate, and with the fuzzy quantity defuzzification, is converted into Control Parameter Δ K
P, Δ K
I, Δ K
DExact value, thereby obtain fuzzy control table;
(8) three output variable Δ K that obtain by fuzzy algorithmic approach
P, Δ K
I, Δ K
D, in conjunction with conventional PID controllers initial parameter K
P0, K
I0, K
D0, bring computing formula K into
P=K
P0+ Δ K
P, K
I=K
I0+ Δ K
I, K
D=K
D0+ Δ K
D, accomplish on-line correction to pid parameter.
In conjunction with Fig. 3 a, Fig. 3 b, in the simulink of matlab module, set up the simulation model of this control algolithm.
Set in this system, every motor speed is 1.01 times of last motor, and promptly the synchronous proportional of four motors relation is ω
1: ω
2: ω
3: ω
4=1:1.01:1.01
2: 1.01
3Wherein the initial speed of first motor is given as 100rad/s, and rotating speed is reduced to 60rad/s when 25s, and other three motors change according to ratio synchronously, when 40s, gives their disturbances of amplitude such as simultaneously.Simulation result is as shown in Figure 4.
Fig. 4 a is the output speed ω of each motor
iFig. 4 b is the tracking error e of each motor
i, e
i(t)=ω
i(t)-ω
i* (t); Fig. 4 c is the ratio synchronous error ε between the motor
I1 *, ε
I1 *(t)=ω
i(t)/μ
i-ω
I-1(t)/μ
I-1, be respectively motor 1 and motor 4, motor 2 and motor 1, motor 3 and motor 2, the ratio synchronous error between motor 4 and the motor 3; Fig. 4 d is synchronous proportional coefficient μ actual between each motor
i, make μ here
1=1, μ then
i(t)=ω
i(t)/ω
1(t).
By finding out among the figure, the tracking error of 4 motors all can converge on zero in 2s, speed change or when disturbance occurring, and waveform can produce beating in various degree, reaches stable but can restrain rapidly, shows that system has convergence and adaptivity preferably; Ratio synchronous error between the motor; When system operation or speed change, can both in 5s, converge on zero, and worst error is no more than 10%, when disturbance occurring wave form varies very little and very rapid convergence to stable; Show that system does not have cumulative errors, have the higher synchronous precision; Synchronous proportional coefficient between the motor except that occurring obviously fluctuation when the speed change, almost remains unchanged when stable operation or disturbance, has embodied the stability of synchronization of system and good robustness.
The present invention also compares this control algolithm and other algorithm, under the constant situation of control structure, controller is realized that by common PID simulated effect is as shown in Figure 5.By finding out that the tracking error of motor and synchronous error convergence time are all correspondingly elongated among the figure, the synchronous error value also increases to 25%, and the synchronous proportional coefficient has obvious fluctuation, and stability reduces, and when speed change, changes greatly the synchronism variation.
Two emulation experiments have shown the control algolithm that the present invention proposes, and in system's operation, speed change or when disturbance occurring, can both realize each motor with certain proportion synchronous operation in fast and stable ground, and net synchronization capability are superior to the control algolithm of routine.
Claims (5)
1. one kind based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model; It is characterized in that: adjacent cross-coupling control structure is improved; Many motor synchronous system are divided into n synchronization subsystem; Define the Control Parameter of each synchronization subsystem in the adjacent cross-coupling control structure, increased proportional component, the Control Parameter to each synchronization subsystem defines again again; And set up the relation equation group of the Control Parameter of definition again, make the synchronous operation in proportion of the rotating speed between each motor in many motor synchronous system; In each synchronization subsystem, comprise 3 parameter fuzzy from the Tuning PID Controller device, realize a tracking error controlled function and two synchronous error controlled function respectively.
2. according to claim 1 based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model, it is following to it is characterized in that adjacent cross-coupling control structure is carried out improved concrete steps:
The first step: establishing synchronous motor has the n platform, should many motor synchronous system be divided into n synchronization subsystem, and division methods is: (1,2,3), and (2,3,4) ..., (i-1, i, i+1) ..., (n-1, n, 1), (n, 1,2), n >=3, i is any motor;
Second step: the tracking error that defines i platform motor is:
e
i(t)=ω
i(t)-ω
i*(t)
Wherein, ω
i(t) be that i platform motor is at t output speed constantly, ω
i* (t) is that i platform motor is in t reference velocity constantly, t>=0;
The 3rd step: the synchronous error ε that defines i platform motor and i-1 platform motor
I1(t) and the synchronous error ε of i platform motor and i+1 platform motor
I2(t) be respectively:
ε
i1(t)=e
i(t)-e
i-1(t)
ε
i2(t)=e
i(t)-e
i+1(t)
The 4th step: the Control Parameter relation equation group of setting up each synchronization subsystem in the adjacent cross-coupling control structure: the tracking error e that makes every motor
i(t), every motor is adjacent the synchronous error ε of two motors
I1(t), ε
I2(t) stable convergence, promptly to satisfy following formula:
The 5th step: when the rotation speed relation between the n platform motor in many motor synchronous system is ω
1: ω
2: ...: ω
n=μ
1: μ
2: ...: μ
nThe time, μ
iBe the proportionality coefficient of i platform motor, μ
i＞0, i ∈ n, the ratio synchronous error that defines between two motors is the difference of each motor after t output speed constantly is divided by proportionality coefficient separately, then the ratio synchronous error ε of i platform motor and i-1 platform motor
I1 *(t) and the ratio synchronous error ε of i platform motor and i+1 platform motor
I2 *(t) be respectively:
ε
i1 *(t)=ω
i(t)/μ
?i-ω
i-1(t)/μ
?i-1
ε
i2 *(t)=ω
i(t)/μ
?i-ω
i+1(t)/μ
?i+1
The 6th step: set up relation equation group: when each subsystem is controlled, should be able to make e through parameter in the adjacent cross-coupling control structure of definition again
i(t), ε
I1 *(t), ε
I2 *(t) converge on zero, promptly will satisfy following formula:
The 7th step: make ω * (t)=ω
i* (t)/μ
i, it is that i platform motor is at the proportionality coefficient of t reference velocity constantly divided by this motor; Unified reference speed value ω * (t) of given each motor of elder generation, then i platform motor is at t reference velocity ω constantly
i* (t) unifies reference speed value ω * (t) for this and multiply by proportionality coefficient μ separately
i, come the reference velocity ω of each motor of control in real time by fuzzy controller again
i* (t).
3. according to claim 1 based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model; It is characterized in that: the parameter fuzzy of said realization tracking error controlled function is the tracking error controller from the Tuning PID Controller device, and the parameter fuzzy of said realization synchronous error controlled function is the synchronous error controller from the Tuning PID Controller device; Definition C
I0Be the tracking error controller of i platform motor, C
I1, C
I2Be two synchronous error controllers of i platform motor; C
I0Deviation be input as e
i(t), be output as u
I0(t), C
I1, C
I2Deviation input be respectively ε
I1 *(t), ε
I2 *(t), output is respectively u
I1(t), u
I2(t), u then
i(t)=u
I0(t)+u
I1(t)+u
I2(t) be the output of i synchronization subsystem, i.e. the speed control amount of i platform motor.
4. according to claim 1ly it is characterized in that the realization of said parameter fuzzy from the Tuning PID Controller device based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model, step is following:
The first step: fuzzy controller is divided into fuzzy controller and parameter can be adjusted PID controller two parts; Fuzzy controller adopts dual input three output control structures; The deviation e of motor speed and deviation variation rate ec are input, and parameter can be adjusted three parameter Δ K of PID controller
P, Δ K
I, Δ K
DBe output; According to the variation of input e, ec, to parameter Δ K
P, Δ K
I, Δ K
DCarry out online self-tuning, obtain new PID controller parameter K
P, K
I, K
DThereby the PID controller is again to the change amount of motor output speed;
Second step: to input e, ec and output Δ K
P, Δ K
I, Δ K
DCarry out obfuscation; Set respectively 7 linguistic variables for NB, NM, NS, Z, PS, PM, PB}, it is negative big that implication is followed successively by, negative in, negative little, zero, just little, the center, honest; Their basic domain, fuzzy domain and quantizing factor are distinguished as follows:
e：(-a,a)，{-6，6}，?K
e=6/a；
ec：(-b,b)，{-6，6}，K
ec=6/b；
ΔK
P：(-c,c)，{-6，6}，K
ΔKP=c/6；
ΔK
I：(-d,d)，{-6，6}，K
ΔKI=d/6；
ΔK
D：(-e,e)，{-6，6}，K
ΔKD=e/6；
Wherein, a, b, c, d, e are the constant greater than 0, K
e, K
Ec, K
Δ KP, K
Δ KI, K
Δ KDBe respectively e, ec, Δ K
P, Δ K
I, Δ K
DQuantizing factor;
The 3rd step: set up input e, ec and output Δ K
P, Δ K
I, Δ K
DMembership function, all get trigonometric function;
The 4th step: formulate fuzzy control rule, and make Δ K
P, Δ K
I, Δ K
DFuzzy control rule table;
The 5th step: the fuzzy control rule of fuzzy reasoning table is generated following conditional statement:
If?e?is?Ai?and?ec?is?Bi,?then?ΔK
P?is?Ci,?ΔK
I?is?Di,?ΔK
D?is?Ei
Ai in the conditional statement, Bi, Ci, Di and Ei are the fuzzy sets of input/output variable, and then fuzzy reasoning table is by the constructed rule base of a series of dual input three output rules;
The 6th step:, carry out fuzzy reasoning according to fuzzy control rule; Fuzzy reasoning adopts " Mamdani " reasoning algorithm, adopts " very big-minimum " composition rule to carry out fuzzy operation, obtains Δ K respectively
P, Δ K
I, Δ K
DThe degree of membership of all fuzzy value under different e and ec, follow the principles into:
μ
U(Z
U)?=?(w
1∧μ
C1(U))∨(w
2∧μ
C2(U))
w
1=μ
A1(E
0)∧μ
B1(EC
0)
w
2=μ
A2(E
0)∧μ
B2(EC
0)
Wherein, " ∧ " for getting little computing, " ∨ " is for getting macrooperation; μ
U(Z
U) be the degree of membership of controlled quentity controlled variable U fuzzy set, controlled quentity controlled variable U comprises Δ K
P, Δ K
I, Δ K
DE
0Be error; EC
0For error changes; μ
A1(E
0), μ
A2(E
0) be E
0To fuzzy set A
1, A
2Degree of membership; μ
B1(EC
0), μ
B2(EC
0) be EC
0To fuzzy set B
1, B
2Degree of membership; μ
C1(U), μ
C2(U) be that controlled quentity controlled variable U is to fuzzy set C
1, C
2Degree of membership;
The 7th step: by e, ec, Δ K
P, Δ K
I, Δ K
DFuzzy subset's degree of membership, and the measured value of error e and error rate ec uses reverse gelatinization gravity model appoach to calculate, and with the fuzzy quantity defuzzification, is converted into Control Parameter Δ K
P, Δ K
I, Δ K
DExact value, thereby obtain fuzzy control table;
The 8th step: three output variable Δ K that obtain by fuzzy algorithmic approach
P, Δ K
I, Δ K
D, in conjunction with PID controller initial parameter K
P0, K
I0, K
D0, bring computing formula K into
P=K
P0+ Δ K
P, K
I=K
I0+ Δ K
I, K
D=K
D0+ Δ K
D, accomplish on-line correction to pid parameter.
5. according to claim 1 or 4 described, it is characterized in that said fuzzy control rule table is following based on the adjacent cross-linked many motors ratio Synchronization Control algorithm of modified model:
。
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