CN111211718B - Automatic parameter adjusting system of active disturbance rejection controller for vector control of permanent magnet synchronous motor - Google Patents
Automatic parameter adjusting system of active disturbance rejection controller for vector control of permanent magnet synchronous motor Download PDFInfo
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Abstract
The invention discloses an auto-disturbance rejection controller parameter automatic regulating system for permanent magnet synchronous motor vector control, which comprises: and the parameter selection index calculation module (COM) of the active disturbance rejection controller and the parameter optimization module (OPT) of the active disturbance rejection controller. The COM is responsible for sampling errors of the rotating speed, the d-axis current and the q-axis current and output signals of active disturbance rejection controllers of the rotating speed, the d-axis current and the q-axis current and calculating a parameter selection index H; and the OPT optimizes the parameters of the 3 controllers by taking H as an objective function, and finally outputs the optimized parameter group of the rotation speed, the d-axis current and the q-axis current active disturbance rejection controller to the controllers. Based on the lion group algorithm, which is a group intelligent algorithm with excellent performance, the invention can realize more efficient parameter self-tuning of the controller; the traditional manual adjustment is replaced by self-tuning, so that the workload of operators is greatly reduced.
Description
Technical Field
The invention belongs to the field of motor control, and particularly relates to an auto-disturbance-rejection controller parameter automatic adjusting system for vector control of a permanent magnet synchronous motor.
Background
A Permanent Magnet Synchronous Motor (PMSM) is a Synchronous motor that generates a Synchronous rotating magnetic field by excitation of Permanent magnets, the Permanent magnets serve as a rotor to generate a rotating magnetic field, and a three-phase stator winding reacts through an armature under the action of the rotating magnetic field to induce three-phase symmetrical current.
Modeling the PMSM, assuming:
(1) no core saturation;
(2) no winding leakage inductance;
(3) no hysteresis and eddy current loss;
(4) no higher harmonics of the magnetic field;
(5) the motor rotor is not provided with a damping winding;
(6) the electrical conductivity of the permanent magnet is zero.
Therefore, a mathematical model of the PMSM in a three-phase coordinate system can be established, wherein the mathematical model comprises a voltage equation, a flux linkage equation, a torque equation and a motion equation.
The voltage equation is
The flux linkage equation is
The torque equation is
Equation of motion of
The three-phase windings of the PMSM are assumed to be in a neutral star connection, and if the three-phase windings are in a triangular connection, the three-phase windings can be equivalently in a star connection. The following constraints can be obtained
It can be seen that under the above conditions, only two phases of the three-phase variables are independent, so the three-phase mathematical model is not the simplest description of the physical object, and therefore it is necessary to replace the two-phase model.
And (3) after the model equation under the three-phase static coordinate system is subjected to Clarke and Park coordinate transformation, a mathematical model of the permanent magnet synchronous motor under a d-q coordinate system can be obtained. As the control system of the PMSM mostly adopts a vector control system, the basic idea of the vector control system is to orient according to the rotor flux linkage, namely, the d axis is coincided with the rotor flux linkage vector, so that a mathematical model of the PMSM under a d-q coordinate system is obtained.
The voltage equation is
The flux linkage equation is
The torque equation is
Te＝pn[ψfiq+(Ld-Lq)idiq] (8)
Equation of motion of
To simplify the control scheme, i is often useddControl strategy is 0. If the d-axis current is 0 at steady state, then the equation at PMSM steady state is
It can be seen that the torque of the motor is only related to the magnitude of the q-axis current, and the electromagnetic torque only has a permanent magnet torque component and does not contain a reluctance torque component. The armature reaction has no direct-axis demagnetization component, and the motor performance can not be deteriorated due to demagnetization. If the voltage component of d-axis is not considered, from uqThe motor is equivalent to a separately excited direct current motor, the d-axis winding is equivalent to open circuit, so that complete decoupling between the d-axis and the motor torque and between the d-axis and the stator winding is realized, a model of the motor is simplified, and control is facilitated.
However, this control strategy has certain disadvantages, such as that when the load increases, the leakage inductance voltage drop increases, and thus the power factor decreases; the lack of reluctance torque can also make the motor unable to reach the theoretical maximum torque, and at the same time unable to achieve flux weakening speed regulation, and the speed regulation range is very limited.
The control strategy is very suitable for a PMSM (permanent magnet synchronous motor) which is small in capacity and narrow in speed regulation range.
idThe PMSM state equation under the control strategy of 0 is
Here, the current i is needed for the rotation speed omega and the d axisdAnd q-axis current iqThese 3 amounts were controlled. PID controllers are commonly used in general industrial applications. The PID control technique has been proposed formally in 1936 by colander (a. Callender) and Stevenson (a. Stevenson) in the uk, and has a very important position in the automatic control technique. The overall control structure of the PMSM vector control system based on the PID controller is shown in fig. 1.
With the rapid development of scientific technology, the requirements on the controller are higher and higher, and the following defects of the traditional PID control technology are gradually revealed:
(1) the error extraction method may cause large initial error and easily cause overshoot;
(2) the differential part is easy to enlarge interference, and pollutes signal distortion to generate huge errors;
(3) the integral link in the classical PID control has obvious effect on inhibiting constant disturbance, but when no disturbance exists, the dynamic characteristic of the system is poor (the closed-loop system has slow reaction and is easy to generate side effects of oscillation and saturation of control quantity), and the inhibition capability of the integral link is not obvious for the disturbance changing along with time;
(4) classical PID control is a weighted combination (i.e., a simple linear combination) of the present (P), past (I), and future (D) of errors, which is not reasonable for many complex controlled objects.
In practical application, the current loop and the rotating speed loop of the PMSM control system based on the PID controller have the problems in many cases, and the PID controller is difficult to apply to occasions with higher requirements on the performance of the motor (such as hypersonic aircrafts, tank fire control systems and the like).
To overcome the defects of the PID Control technology, mr. hangul has formally proposed Auto-Disturbance Rejection Control (ADRC) in 2002. The active disturbance rejection control uses the 'error feedback to eliminate errors' in classical control, and uses the observation of system state quantity in modern control. By combining the essences of the two control methods, the active disturbance rejection controller can control various objects quickly, accurately, without overshoot and stably.
ADRC is used in many fields, such as hypersonic aircrafts, magnetic levitation, superconducting particle accelerators, large radio telescopes, tank fire control systems, nuclear power station cooling systems and the like, and industrial robots, servo motor drivers, unmanned plane flight control systems, sweeping robots, even website search engines and the like.
The active disturbance rejection controller mainly comprises a Tracking Differentiator (TD), a nonlinear state error feedback control law (NLSEF) and an Extended State Observer (ESO).
TD: the transition process of the closed-loop system is arranged through the TD, and the overshoot of the system is reduced. Because discontinuous and random noise input signals often appear in actual engineering, a complex input tracking signal can be transited into a stable continuous input signal through extracting a signal and arranging a reasonable transition process, so that the control quality of the system is improved.
NLSEF: the PID controller calculates the error signal by using a linear superposition method with the simplest form, and the control efficiency and effect are difficult to meet the requirements of people, so the active disturbance rejection controller performs nonlinear calculation on the error signal through NLSEF. The control performance of the linear superposition method for processing the error signal is not as good as that of a nonlinear algorithm of NLSEF through mathematical derivation and simulation.
ESO: the observer is difficult to directly observe the state of the system, so that the internal state and interference of the system are observed in real time through the ESO, and the controllable and observable performance of the system is greatly improved. The interference is external interference suffered by the system and interference factors in the system, and the internal state of the system and the state of an unmodeled part in the system are directly estimated from the output quantity of the system only by setting parameters in a proper extended state observer, so that the interference is estimated and compensated in real time.
In summary, the improved active disturbance rejection controllers in the three parts can improve the response speed, reduce overshoot, and control a nonlinear system which is difficult to control by other controllers with high precision, thereby improving the application capability of the nonlinear system in practical engineering.
When the controlled object is a first-order system, the state equation is assumed to be
Where x is a state variable, f (x) is a polynomial associated with the state variable x, u is a control quantity, and w is a disturbance quantity. Then the expressions for TD, NLSEF, and ESO of the active disturbance rejection controller are
Wherein
r is a velocity factor, determining the tracking velocity. The larger r, the faster the tracking speed, but at the same time, the overshoot is increased.
k1For feedback gain, increase k1The response speed of the system can be increased, but if the value is too large, the system can oscillate or even be unstable.
β1And beta2To be transportedAnd an error correction factor is generated, and the dynamic performance of the system is greatly influenced. Estimation of state variables is primarily governed by beta1The estimation of the system disturbance is mainly influenced by beta2The influence of (c). Beta is a1And beta2The larger the state variable and the faster the estimate of the system disturbance will converge; however, if the value is too large, the output of the ESO will generate oscillation divergence and generate a high frequency noise signal.
Alpha is a nonlinear factor, and the smaller alpha, the stronger the nonlinearity of the fal function. Alpha is alpha1、α2、α3Respectively TD nonlinear factor, NLSEF nonlinear factor and ESO nonlinear factor.
Delta is a filtering factor, and increasing delta can make the filtering effect better, but also increases the delay of tracking. Delta1、δ2、δ3TD filter factor, NLSEF filter factor, ESO filter factor.
Thus, when the controlled object is a first-order system, the active disturbance rejection controller contains r, α1，δ1，k1，α2，δ2，β1，β2，α3And delta3These 10 parameters to be adjusted (parameter b is determined by the controlled object and no adjustment is needed). The structure of the active disturbance rejection controller is shown in fig. 2.
And designing an active disturbance rejection controller for a rotating speed loop, a d-axis current loop and a q-axis current loop of the PMSM.
The rotating speed loop state equation of PMSM is
It is written in the form shown in formula (12)
Wherein
Thus, the expressions for the speed loop TD, NLSEF and ESO can be written as
Wherein r1, α 11, δ 11, k11, α 21, δ 21, β 11, β 21, α 31, and δ 31 are the speed factor, TD nonlinear factor, TD filter factor, feedback gain, NLSEF nonlinear factor, NLSEF filter factor, output error correction factor, ESO nonlinear factor, and ESO filter factor of the rotational speed active disturbance rejection controller, respectively.
The d-axis current loop state equation of PMSM is
It is written in the form shown in formula (12)
Wherein
Thus the expressions for d-axis current loop TD, NLSEF, and ESO can be written as
Wherein r2, α 12, δ 12, k12, α 22, δ 22, β 12, β 22, α 32, δ 32 are the speed factor, TD nonlinear factor, TD filter factor, feedback gain, NLSEF nonlinear factor, NLSEF filter factor, output error correction factor, ESO nonlinear factor, ESO filter factor of the d-axis current active disturbance rejection controller, respectively.
The q-axis current loop state equation of PMSM is
It is written in the form shown in formula (12)
Wherein
Thus the expressions for the q-axis current loop TD, NLSEF and ESO can be written as
Wherein r3, α 13, δ 13, k13, α 23, δ 23, β 13, β 23, α 33, and δ 33 are the speed factor, TD nonlinear factor, TD filter factor, feedback gain, NLSEF nonlinear factor, NLSEF filter factor, output error correction factor, ESO nonlinear factor, and ESO filter factor of the q-axis current active disturbance rejection controller, respectively.
The structure of the PMSM vector control system based on the active disturbance rejection control is shown in fig. 3.
Although the performance of the auto-disturbance-rejection controller has proved to be superior to that of the conventional PID controller in many experiments, its parameters to be tuned are excessive. There are 3 total active disturbance rejection controllers, each controller has 10 parameters to be adjusted, and then there are 30 parameters to be adjusted for 3 controllers, if the manual adjustment mode is adopted, the workload is very large, and the manual adjustment is difficult to achieve the optimal control effect.
Disclosure of Invention
Aiming at the technical defects in the prior art, the invention provides an auto-disturbance-rejection controller parameter automatic adjusting device for vector control of a permanent magnet synchronous motor. The device can realize more efficient parameter self-tuning of the controller based on the superior group intelligent algorithm such as the lion group algorithm, and the workload of operators is greatly reduced.
The invention relates to an auto-disturbance rejection controller parameter automatic regulating system for permanent magnet synchronous motor vector control, which comprises an auto-disturbance rejection controller parameter selection index calculating module (COM) and an auto-disturbance rejection controller parameter optimizing module (OPT);
the parameter selection index calculation module (COM) of the active disturbance rejection controller obtains a rotating speed error (e)1) Rotational speed auto-disturbance rejection controller (ADRC)1) Is output signal (u)1) D-axis current error (e)2) D-axis current auto-disturbance rejection controller (ADRC)2) Is output signal (u)2) Q-axis current error (e)3) Q-axis current auto-disturbance rejection controller (ADRC)3) Is output signal (u)3) (ii) a Output active disturbance rejection controlSelecting an index H from the device parameters; the working state of an auto-disturbance rejection controller parameter selection index calculation module (COM) is controlled by a driving signal;
the parameter optimizing module (OPT) of the active disturbance rejection controller obtains an active disturbance rejection controller parameter selection index H, and outputs 3 groups of optimized controller parameter groups, namely a parameter group (p) of the rotating speed active disturbance rejection controller1) D-axis current auto-disturbance rejection controller parameter set (p)2) Q-axis current auto-disturbance-rejection controller parameter set (p)3)。
As a preferred scheme of the present invention, the process of the auto-disturbance rejection controller parameter selection index calculation module (COM) for obtaining the auto-disturbance rejection controller parameter selection index H specifically includes:
setting the parameter selection index of the rotation speed active disturbance rejection controller as H1By selecting e in real time1、u1And a rise time t of the rotational speedu1To H1And (6) performing calculation. H1Is expressed as
Wherein, TsFor a sampling period, TaFor the total sampling time of the controller state in one iteration of the OPT, int is a floor function, w11、w21、w31Is the weight; e.g. of the type1(k) Is e1In discrete form u1(k) Is u1In discrete form.
Setting the parameter selection index of the d-axis current active disturbance rejection controller as H2By selecting e in real time2、u2And d-axis current rise time tu2To H2Is calculated, H2Is expressed as
Wherein, w12、w22、w32As a weight value, e2(k) Is e2In discrete form u2(k) Is u2In discrete form.
Setting the parameter selection index of the q-axis current active disturbance rejection controller as H3By selecting e in real time3、u3And q-axis current rise time tu3To H3Is calculated, H3Is expressed as
Wherein, w13、w23、w33As a weight value, e3(k) Is e3In discrete form u3(k) Is u3In discrete form.
COM then passes through pair H1、H2、H3Carrying out weighted summation to obtain a comprehensive evaluation index H capable of comprehensively measuring the performances of 3 active disturbance rejection controllers, wherein the expression of H is as follows
Wherein c is1、c2、c3Is a weight; the smaller H, the better the controller performance.
In a preferred embodiment of the present invention, the rotation speed rise time t isu1The time required for the first time the rotational speed ω reaches the steady state value from zero; d-axis current rise time tu2Is d-axis current idThe time required to reach a steady state value for the first time from zero; q-axis current rise time tu3Is q-axis current iqThe time required to reach the steady state value for the first time from time zero.
As a preferred embodiment of the present invention, the process of the auto disturbance rejection controller parameter optimization module (OPT) for obtaining 3 sets of optimized controller parameter sets specifically includes:
OPT takes H as an objective function to optimize 30 parameters of 3 controllers, and the controller parameter optimization problem is equivalent to a function maximum solving problem:
s.t.pimin≤pi≤pimax(i＝1,2,3)
wherein p isimaxFor the upper limit, p, of the 3 ADRC parameter setsiminThe lower limit of the 3 active disturbance rejection controller parameter sets, i is 1,2 and 3; are respectively expressed as
pi＝(ri,α1i,δ1i,k1i,α2i,δ2i,β1i,β2i,α3i,δ3i)
pimax＝(rimax,α1imax,δ1imax,k1imax,α2imax,δ2imax,β1imax,β2imax,α3imax,δ3imax)
pimin＝(rimin,α1imin,δ1imin,k1imin,α2imin,δ2imin,β1imin,β2imin,α3imin,δ3imin)
r is a velocity factor, k1For feedback gain, beta1And beta2For outputting the error correction factor, alpha is a non-linear factor, alpha1、α2、α3TD nonlinear factor, NLSEF nonlinear factor and ESO nonlinear factor; delta is the filter factor, delta1、δ2、δ3TD filter factor, NLSEF filter factor, ESO filter factor.
As a preferred embodiment of the present invention, the OPT adjusts the controller parameters based on the lion group algorithm to combine 3 sets of parameters (p)1,p2,p3) As the position information of the lions, the fitness function for representing the degree of goodness of the position of each lion is taken as
Wherein epsilon0Is a constant greater than 0 such that the denominator of the fitness function is not 0. The larger f, the better the position, i.e. parameter, of each lion;
stopping optimization after the iteration end condition is reached, and outputting the final controller parameter group p1、p2、p3To the controller.
As a preferred scheme of the invention, the OPT is based on a lion group algorithm, so that the position x of each lioniIs composed of
xi＝(p1,p2,p3)
The steps of adjusting the controller parameters are as follows:
step 9, initializing the position x of the lion in the lion groupiThe number N, the maximum iteration number T, the dimension space D and the scale factor beta of the adult lion in the lion group are calculated;
step 10, calculating the number of the lion king, the adult female lion and the young lion, setting the individual historical optimal position as the current position of each lion, and setting the initial group optimal position as the lion king position;
step 11, updating the position of the lion king and calculating a fitness value;
step 12, updating the position of the female lion;
step 13, updating the position of the young lion;
step 14, calculating a fitness value according to the position of the lion, updating the historical optimal position of the lion and the historical optimal position of the lion group, judging whether the algorithm meets an end condition, and turning to Step 8 if the algorithm meets the end condition, or turning to Step 7 if the algorithm does not meet the end condition;
step 15, reordering every certain iteration number, determining the positions of the lion king, the adult female lion and the young lion, and turning to Step 3;
and Step 16, outputting the position of the lion king, namely the optimal solution of the solved problem, and ending the algorithm.
Stopping optimization after the iteration end condition is reached, and outputting the final controller parameter group p1,p2,p3To the controller, the control system is then ready for use. The structure of the PMSM vector control system with automatic parameter adjustment is shown in fig. 4.
The beneficial technical effects of the invention are as follows:
(1) because the parameters of the active disturbance rejection controller are many, and the number of the active disturbance rejection controller is up to 30 in the present example, the invention can carry out automatic adjustment on the active disturbance rejection controller, thereby replacing the traditional manual adjustment, greatly reducing the workload of debugging personnel, avoiding accidental errors which are possibly generated by the manual adjustment, and being very suitable for being applied to a complex multi-parameter control system such as a PMSM vector control system based on the active disturbance rejection control.
(2) The auto-disturbance rejection controller parameter optimization module (OPT) in the invention is based on the lion group algorithm. Compared with the traditional swarm intelligence algorithms such as an ant colony algorithm, a particle swarm algorithm, an artificial fish swarm algorithm, a mixed frog-leaping algorithm, a firefly optimization algorithm, a wolf colony algorithm, an artificial bee colony algorithm and the like, the lion colony algorithm has higher convergence speed, higher precision and stronger global search capability. Therefore, the OPT based on the algorithm can theoretically greatly improve the parameter optimization efficiency compared with a parameter optimization module based on a traditional algorithm.
Drawings
FIG. 1 is a block diagram of a PMSM vector control system based on a PID controller;
FIG. 2 is a block diagram of an active disturbance rejection controller;
FIG. 3 is a block diagram of an active disturbance rejection controller based PMSM vector control system;
FIG. 4 is a block diagram of a PMSM vector control system with an automatic parameter adjustment system;
fig. 5 is a waveform diagram of a pulse type rotational speed setting signal.
Detailed Description
In order to describe the present invention more specifically, the following detailed description will be made of the technical solutions of the present invention and the related working principles.
As shown in fig. 1, an auto-disturbance rejection controller parameter automatic adjustment system for vector control of a permanent magnet synchronous motor includes an auto-disturbance rejection controller parameter selection index calculation module (COM).
COM obtains the rotation speed error (e)1) Rotational speed auto-disturbance rejection controller (ADRC)1) Is output signal (u)1) D-axis current error (e)2) D-axis current auto-disturbance rejection controller (ADRC)2) Is output signal (u)2) Q-axis current error (e)3) Q-axis current auto-disturbance rejection controller (ADRC)3) Is output signal (u)3) (ii) a Outputting parameters of the active disturbance rejection controller to select an index H; the working state of an auto-disturbance rejection controller parameter selection index calculation module (COM) is controlled by a driving signal;
the COM calculates the obtained signals to obtain a comprehensive evaluation index H which can comprehensively measure the performance of the 3 active disturbance rejection controllers, and the expression of the H is as follows
The smaller H, the better the control performance of the controller. The control performance of 3 controllers is represented by an index H, so that the complexity of an evaluation system is greatly reduced.
As shown in fig. 1, an auto-disturbance rejection controller parameter automatic adjustment system for vector control of a permanent magnet synchronous motor includes an auto-disturbance rejection controller parameter optimization module (OPT).
OPT obtains the parameter selection index H of the active disturbance rejection controller, and outputs 3 groups of optimized controller parameter groups, namely the parameter group (p) of the rotating speed active disturbance rejection controller1) D-axis current auto-disturbance rejection controller parameter set (p)2) Q-axis current auto-disturbance-rejection controller parameter set (p)3)。
OPT takes H as an objective function to optimize 30 parameters of 3 controllers, and the controller parameter optimization problem is equivalent to a function maximum solving problem
s.t.pimin≤pi≤pimax(i＝1,2,3)
Wherein p isi(i-1, 2,3) are a rotation speed active-disturbance-rejection controller, a d-axis current active-disturbance-rejection controller and a q-axis current active-disturbance-rejection controller respectivelyController 3 parameter sets of the active disturbance rejection controller, pimax(i is 1,2,3) is the upper limit, p, of the 3 sets of auto-disturbance-rejection controller parametersimin(i ═ 1,2,3) is the lower limit of the 3 sets of auto-disturbance-rejection controller parameters; are respectively expressed as
pi＝(ri,α1i,δ1i,k1i,α2i,δ2i,β1i,β2i,α3i,δ3i)
pimax＝(rimax,α1imax,δ1imax,k1imax,α2imax,δ2imax,β1imax,β2imax,α3imax,δ3imax)
pimin＝(rimin,α1imin,δ1imin,k1imin,α2imin,δ2imin,β1imin,β2imin,α3imin,δ3imin)
The OPT carries out parameter optimization based on a lion group algorithm. Position x of each lion in the algorithmiIs composed of
xi＝(p1,p2,p3)
The steps of adjusting the controller parameters are as follows:
step 17, initializing the position x of the lion in the lion groupiThe number N, the maximum iteration number T, the dimension space D and the scale factor beta of the adult lion in the lion group are calculated;
step 18, calculating the number of the lion king, the adult female lion and the young lion, setting the individual historical optimal position as the current position of each lion, and setting the initial group optimal position as the lion king position;
step 19, updating the position of the lion king and calculating a fitness value;
step 20, updating the position of the female lion;
step 21, updating the position of the young lion;
step 22, calculating a fitness value according to the position of the lion, updating the historical optimal position of the lion and the historical optimal position of the lion group, judging whether the algorithm meets an end condition, and turning to Step 8 if the algorithm meets the end condition, or turning to Step 7 if the algorithm does not meet the end condition;
step 23, reordering every certain iteration number, determining the positions of the lion king, the adult female lion and the young lion, and turning to Step 3;
and Step 24, outputting the position of the lion king, namely the optimal solution of the solved problem, and ending the algorithm.
The OPT carries out parameter optimization based on a lion group algorithm. Compared with the traditional group intelligent algorithm, the lion group algorithm has higher convergence speed, higher precision and stronger global search capability. Therefore, the OPT based on the algorithm can theoretically greatly improve the parameter optimization efficiency compared with a parameter optimization module based on a traditional algorithm.
When the parameters of the active disturbance rejection controller are optimized, the automatic parameter adjusting system is started through the driving signal, and then the parameters of the controller can be automatically optimized by adopting a pulse type rotating speed setting signal. And outputting the final parameters to the controller after the optimization process is finished, and closing the automatic parameter adjusting system through a driving signal.
The pulse type rotational speed setting signal is shown in fig. 5. Wherein t is1Optimizing the time, t, for the parameter2For system recovery time, T is the pulse period. t is t1In a time period, the rotating speed given signal is equivalent to a step signal, the H value can be obtained by calculation after the state of 3 controllers is sampled through COM in the time period, and then the COM outputs the H value to OPT for parameter optimization; t is t2The rotation speed setting signal is set to 0 for a period of time long enough to allow the system to return to approximately the state at time 0. The period of the pulse is T, the OPT is iterated once after each period, the optimization is stopped until the end condition is met, and the final parameters are output to the controller and put into use. The automatic parameter adjusting system realizes automatic adjustment of parameters, reduces the workload of operators, avoids accidental errors which are possibly generated by manual adjustment, and is very suitable for being applied to a complex multi-parameter control system such as a PMSM vector control system based on an active disturbance rejection controller.
Claims (4)
1. An automatic parameter adjusting system of an active disturbance rejection controller for vector control of a permanent magnet synchronous motor is characterized by comprising an active disturbance rejection controller parameter selection index calculating module COM and an active disturbance rejection controller parameter optimizing module OPT;
the parameter selection index calculation module COM of the active disturbance rejection controller obtains the rotating speed error e1ADRC (active disturbance rejection controller) for rotating speed1Output signal u of1D-axis current error e2D-axis current auto-disturbance rejection controller ADRC2Output signal u of2Q-axis current error e3Q-axis current auto-disturbance rejection controller ADRC3Output signal u of3(ii) a Outputting parameters of the active disturbance rejection controller to select an index H; the working state of the parameter selection index calculation module COM of the active disturbance rejection controller is controlled by a driving signal;
the process of the parameter selection index H of the active disturbance rejection controller by the parameter selection index calculation module COM specifically comprises the following steps:
setting the parameter selection index of the rotation speed active disturbance rejection controller as H1By selecting e in real time1、u1And a rise time t of the rotational speedu1To H1Is calculated, H1Is expressed as
Wherein, TsFor a sampling period, TaFor the total sampling time of the controller state in one iteration of the OPT, int is a floor function, w11、w21、w31Is the weight; e.g. of the type1(k) Is e1In discrete form u1(k) Is u1A discrete form of (a);
setting the parameter selection index of the d-axis current active disturbance rejection controller as H2By selecting e in real time2、u2And d-axis current rise time tu2To H2Is calculated, H2Is expressed as
Wherein, w12、w22、w32As a weight value, e2(k) Is e2In discrete form u2(k) Is u2A discrete form of (a);
setting the parameter selection index of the q-axis current active disturbance rejection controller as H3By selecting e in real time3、u3And q-axis current rise time tu3To H3Is calculated, H3Is expressed as
Wherein, w13、w23、w33As a weight value, e3(k) Is e3In discrete form u3(k) Is u3A discrete form of (a);
COM then passes through pair H1、H2、H3Carrying out weighted summation to obtain a comprehensive evaluation index H capable of comprehensively measuring the performances of 3 active disturbance rejection controllers, wherein the expression of H is as follows
Wherein c is1、c2、c3Is a weight; the smaller H, the better the controller performance;
the parameter optimizing module OPT of the active disturbance rejection controller obtains an active disturbance rejection controller parameter selection index H and outputs 3 groups of optimized controller parameter groups, namely a parameter group p of the rotating speed active disturbance rejection controller1D-axis current auto-disturbance rejection controller parameter set p2Q-axis current auto-disturbance rejection controller parameter set p3；
The process of solving 3 sets of optimized controller parameter sets by the auto disturbance rejection controller parameter optimizing module OPT specifically comprises:
OPT takes H as an objective function to optimize 30 parameters of 3 controllers, and the controller parameter optimization problem is equivalent to a function maximum solving problem:
s.t.pimin≤pi≤pimax(i＝1,2,3)
wherein p isimaxFor the upper limit, p, of the 3 ADRC parameter setsiminThe lower limit of the 3 active disturbance rejection controller parameter sets is 1,2 and 3, and the expressions are respectively
pi＝(ri,α1i,δ1i,k1i,α2i,δ2i,β1i,β2i,α3i,δ3i)
pimax＝(rimax,α1imax,δ1imax,k1imax,α2imax,δ2imax,β1imax,β2imax,α3imax,δ3imax)
pimin＝(rimin,α1imin,δ1imin,k1imin,α2imin,δ2imin,β1imin,β2imin,α3imin,δ3imin)
r is a velocity factor, k1For feedback gain, beta1And beta2For outputting the error correction factor, alpha is a non-linear factor, alpha1、α2、α3TD nonlinear factor, NLSEF nonlinear factor and ESO nonlinear factor; delta is the filter factor, delta1、δ2、δ3TD filter factor, NLSEF filter factor, ESO filter factor.
2. The auto-disturbance-rejection controller parameter auto-regulation system for PMSM vector control according to claim 1, characterized in that the speed rise time tu1The time required for the first time the rotational speed ω reaches the steady state value from zero; d-axis current rise time tu2Is d-axis current idFrom zeroThe time required for the moment to reach the steady state value for the first time; q-axis current rise time tu3Is q-axis current iqThe time required to reach the steady state value for the first time from time zero.
3. Auto-disturbance-rejection controller parameter auto-adjustment system for PMSM vector control according to claim 2, characterized in that the OPT adjusts the controller parameters based on lion-group algorithm, with a combination of 3 sets of parameters (p)1,p2,p3) As the position information of the lions, the fitness function for representing the degree of goodness of the position of each lion is taken as
Wherein epsilon0A constant greater than 0 is adopted, so that the denominator of the fitness function is not 0, and the larger f is, the better each lion is the position, namely the parameter is;
stopping optimization after the iteration end condition is reached, and outputting the final controller parameter group p1、p2、p3To the controller.
4. The ADRC parameter auto-tuning system of claim 3, wherein the OPT is based on a lion-group algorithm, so that the position x of each lion is determinediIs composed of
xi＝(p1,p2,p3)
The steps of adjusting the controller parameters are as follows:
step 1, initializing the position x of the lion in the lion groupiThe number N, the maximum iteration number T, the dimension space D and the scale factor beta of the adult lion in the lion group are calculated;
step 2, calculating the number of the lion king, the adult female lion and the young lion, setting the individual historical optimal position as the current position of each lion, and setting the initial group optimal position as the lion king position;
step 3, updating the position of the lion king and calculating a fitness value;
step 4, updating the position of the female lion;
step 5, updating the position of the young lion;
step 6, calculating a fitness value according to the position of the lion, updating the historical optimal position of the lion and the historical optimal position of the lion group, judging whether the algorithm meets an end condition, and turning to Step 8 if the algorithm meets the end condition, or turning to Step 7;
step 7, reordering every certain iteration number, determining the positions of the lion king, the adult female lion and the young lion, and turning to Step 3;
and Step 8, outputting the position of the lion king, namely the optimal solution of the solved problem, and ending the algorithm.
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