阅读说明：本技术 一种永磁同步电机转速环自抗扰控制器参数整定方法 (Parameter setting method for rotating speed ring active disturbance rejection controller of permanent magnet synchronous motor ) 是由 杜昭平 李伟 吴伟 王伟然 伍雪冬 李建祯 于 2019-11-08 设计创作，主要内容包括：本发明公开了一种永磁同步电机转速环自抗扰控制器参数整定方法,建立转速和电流双闭环控制结构,根据Tent映射反向学习初始化灰狼种群策略,采用随迭代次数增加而非线性变化的收敛因子,并在算法中狼群位置更新环节引入levy飞行策略进行变异操作,最终获得整定后的参数。本发明从初始化改进灰狼优化算法种群,根据帐篷映射反向学习策略对灰狼种群初始化、设计一种随迭代次数增加非线性变化的收敛因子和在狼群位置更新环节引入levy飞行策略三方面提出了一种改进灰狼优化算法,可以提高算法初始种群的多样性,对复杂搜索的适应性与调节性更好,可以避免陷入局部最优,提高算法的收敛速度与全局寻优能力,效果较其它改进灰狼优化算法更优。(The invention discloses a parameter setting method for a permanent magnet synchronous motor rotating speed ring active disturbance rejection controller, which comprises the steps of establishing a rotating speed and current double closed loop control structure, initializing a gray wolf population strategy according to Tent mapping reverse learning, adopting a convergence factor which changes nonlinearly along with the increase of iteration times, introducing a levy flight strategy in a wolf population position updating link in an algorithm for variation operation, and finally obtaining a set parameter. The invention provides an improved gray wolf optimization algorithm from the three aspects of initializing an improved gray wolf optimization algorithm population, initializing the gray wolf population according to a tent mapping reverse learning strategy, designing a convergence factor which increases nonlinear change along with the number of iterations and introducing a levy flight strategy in a wolf population position updating link, which can improve the diversity of the initial population of the algorithm, has better adaptability and adjustability to complex search, can avoid falling into local optimum, improves the convergence speed and the global optimizing capability of the algorithm, and has better effect than other improved gray wolf optimization algorithms.)

1. A parameter setting method for an active disturbance rejection controller of a rotating speed ring of a permanent magnet synchronous motor is characterized by comprising the following steps:

step 1: a permanent magnet synchronous motor control closed loop with a first-order active disturbance rejection speed controller is constructed by adopting a speed outer loop and a current inner loop;

step 2: setting parameters for improving the gray wolf optimization algorithm, including setting the scale of a gray wolf population, the dimension of a search aperture of the gray wolf population, the maximum iteration times of the algorithm, parameters which do not need to be set in an active disturbance rejection controller and termination conditions;

and step 3: initializing a grey wolf population in an improved grey wolf optimization algorithm by adopting a tent mapping reverse learning strategy, sequentially taking a position vector of each grey wolf individual in the grey wolf population as a parameter to be set of an active disturbance rejection controller, simulating a permanent magnet synchronous motor control closed loop constructed in the step 1, and executing an iterative process from the step 4 to the step 7;

and 4, step 4: calculating the fitness function value of each individual grey wolf according to the fitness function, sequencing the fitness function values of all the individual grey wolf, respectively marking the grey wolf individuals in the first three as alpha wolf, beta wolf and delta wolf, and marking the rest grey wolf individuals as omega wolf;

and 5: acquiring a convergence factor of nonlinear change according to the current iteration times, and then acquiring coefficient vectors A and C;

step 6: updating the omega wolf according to the positions of the alpha wolf, the beta wolf and the delta wolf and the coefficient vectors A and C to obtain a new grey wolf population, then performing variation operation on all grey wolf individuals in the new grey wolf population by adopting a levy flight strategy, and updating the positions of the grey wolf individuals according to the current fitness function values of the grey wolf individuals;

and 7: and when the stopping condition is met, outputting the optimal parameters of the active disturbance rejection controller and the corresponding fitness function value, and stopping iteration.

2. The method for setting parameters of the active disturbance rejection controller of the rotating speed ring of the permanent magnet synchronous motor according to claim 1, wherein the specific method for initializing the gray wolf population in the improved gray wolf optimization algorithm by adopting the tent mapping reverse learning strategy in the step 3 is as follows: firstly, initializing a wolf population by adopting tent mapping, and generating a chaos sequence x ═ x in a D-dimensional space_dD is 1,2, D, the generated chaos sequence is processed by the formula X_min+x_t·(X_max-X_min) Mapping the mapping to the value range of the wolf population to obtain X of the wolf population, wherein X is_minAnd X_maxSearching an upper boundary and a lower boundary for X, then generating a reverse gray wolf population OX by the X of the gray wolf population through reverse learning, finally combining the gray wolf population and the reverse gray wolf population to obtain a new gray wolf population, calculating fitness function values of all gray wolf individuals in the new gray wolf population and sequencing, selecting a plurality of gray wolf individuals with the best fitness function values as initial gray wolf populations, and finishing initialization of the gray wolf populations.

3. The method for setting parameters of the active disturbance rejection controller of the rotating speed ring of the permanent magnet synchronous motor according to claim 2, wherein in the step 3, the grey wolf population is reversely learned according to the following formula,

where p is the inverse learning probability.

4. The method for setting parameters of the active disturbance rejection controller of the rotating speed ring of the permanent magnet synchronous motor according to claim 1, wherein the fitness function in the step 4 is as follows:

J＝w₁[Δω]²+w₂[e(t)]²

wherein, w₁And w₂Is a weight.

5. The method for setting parameters of the active-disturbance-rejection controller for the rotating speed ring of the permanent magnet synchronous motor according to claim 1, wherein in the step 5, a convergence factor of nonlinear change is obtained according to the current iteration number, and specific formulas for obtaining coefficient vectors A and C are as follows:

wherein: m is a non-linear adjustment coefficient, t is the current iteration number, and M is the maximum iteration number.

6. The method for setting parameters of the active disturbance rejection controller of the rotating speed ring of the permanent magnet synchronous motor according to claim 1, wherein the method for updating the position of the individual grayish wolf according to the current fitness function value of the individual grayish wolf in the step 6 comprises the following steps: and when the current fitness function value of the grey wolf individual is smaller than the fitness function value of the grey wolf individual before the mutation operation, updating the position of the grey wolf individual.

Technical Field

The invention relates to the technical field of synchronous motors, in particular to a method for setting parameters of a rotating speed ring active disturbance rejection controller of a permanent magnet synchronous motor based on an IGWO algorithm.

Background

Compared with a common motor, the permanent magnet synchronous motor has the characteristics of small volume, simple structure, high torque-inertia ratio and high reliability, and has been widely applied to the field of industrial automation by virtue of the performance advantages of the permanent magnet synchronous motor. However, the permanent magnet synchronous motor is a multivariable and strongly coupled nonlinear system, and meanwhile, there are also factors such as parameter variation and load disturbance, and the conventional PID control is difficult to meet the real control requirements, which may cause the reduction of the rotation speed control performance of the permanent magnet synchronous motor and affect the control effect of the system.

In recent years, researchers at home and abroad have adopted a plurality of control methods for solving the problem, such as: fuzzy control, reverse control, self-adaptive control, sliding mode control and the like. The active disturbance rejection control technology combines classical PID control with modern control theory, is a nonlinear control algorithm adopting dynamic linear compensation, and has the most prominent characteristic that all uncertain factor effects acting on a controlled object are classified as unknown disturbance, and the unknown disturbance is estimated and compensated by input and output information of the object, so that the purpose of automatic disturbance rejection is achieved. The active disturbance rejection control does not need to directly measure the external disturbance action and know the disturbance rule in advance, and by utilizing the characteristic of the active disturbance rejection control, the coupling action among the subsystems of the multivariable system can be classified as unknown disturbance as an uncertain factor to carry out decoupling control, so that the system can effectively inhibit the influence caused by various disturbances, and the aim of accurate control is fulfilled.

The control performance of the active disturbance rejection controller depends on parameters inside the controller, and how to set a plurality of parameters to enable the controller to work in an optimal state is a difficult problem in the active disturbance rejection application. The traditional empirical method has poor setting effect, and learners also introduce fuzzy control to set parameters, but the design of fuzzy rules is difficult.

Disclosure of Invention

The invention provides a parameter setting method for a rotating speed ring active-disturbance-rejection controller of a permanent magnet synchronous motor, which aims to solve the problems of difficult parameter setting, low rotating speed control precision of a system and low responsiveness and stability of the rotating speed ring active-disturbance-rejection controller in the permanent magnet synchronous motor in the prior art.

The invention provides a parameter setting method for a rotating speed ring active disturbance rejection controller of a permanent magnet synchronous motor, which comprises the following steps:

step 1: a permanent magnet synchronous motor control closed loop with a first-order active disturbance rejection speed controller is constructed by adopting a speed outer loop and a current inner loop;

step 2: setting parameters for improving the gray wolf optimization algorithm, including setting the scale of a gray wolf population, the dimension of a search aperture of the gray wolf population, the maximum iteration times of the algorithm, parameters which do not need to be set in an active disturbance rejection controller and termination conditions;

and step 3: initializing a grey wolf population in an improved grey wolf optimization algorithm by adopting a tent mapping reverse learning strategy, sequentially taking a position vector of each grey wolf individual in the grey wolf population as a parameter to be set of an active disturbance rejection controller, simulating a permanent magnet synchronous motor control closed loop constructed in the step 1, and executing an iterative process from the step 4 to the step 7;

and 4, step 4: calculating the fitness function value of each individual grey wolf according to the fitness function, sequencing the fitness function values of all the individual grey wolf, respectively marking the grey wolf individuals in the first three as alpha wolf, beta wolf and delta wolf, and marking the rest grey wolf individuals as omega wolf;

and 5: acquiring a convergence factor of nonlinear change according to the current iteration times, and then acquiring coefficient vectors A and C;

step 6: updating the omega wolf according to the positions of the alpha wolf, the beta wolf and the delta wolf and the coefficient vectors A and C to obtain a new grey wolf population, then performing variation operation on all grey wolf individuals in the new grey wolf population by adopting a levy flight strategy, and updating the positions of the grey wolf individuals according to the current fitness function values of the grey wolf individuals;

and 7: and when the stopping condition is met, outputting the optimal parameters of the active disturbance rejection controller and the corresponding fitness function value, and stopping iteration.

Further, the specific method for initializing the sirius population in the improved sirius optimization algorithm by adopting the tent mapping reverse learning strategy in the step 3 is as follows: firstly, initializing a wolf population by adopting tent mapping, and generating a chaos sequence x ═ x in a D-dimensional space_dD is 1,2, D, the generated chaos sequence is processed by the formula X_min+x_t·(X_max-X_min) Mapping the mapping to the value range of the wolf population to obtain X of the wolf population, wherein X is_minAnd X_maxSearching an upper boundary and a lower boundary for X, then generating a reverse gray wolf population OX by the X of the gray wolf population through reverse learning, finally combining the gray wolf population and the reverse gray wolf population to obtain a new gray wolf population, calculating fitness function values of all gray wolf individuals in the new gray wolf population and sequencing, selecting a plurality of gray wolf individuals with the best fitness function values as initial gray wolf populations, and finishing initialization of the gray wolf populations.

Further, in the step 3, the wolf population is reversely learned according to the following formula,

where p is the inverse learning probability.

Further, the fitness function in step 4 is as follows:

J＝w₁[Δω]²+w₂[e(t)]²

wherein, w₁And w₂Is a weight.

Further, in step 5, a convergence factor of the nonlinear change is obtained according to the current iteration number, and a specific formula of the coefficient vectors a and C is obtained as follows:

wherein: m is a non-linear adjustment coefficient, t is the current iteration number, and M is the maximum iteration number.

Further, in the step 6, the method for updating the position of the individual gray wolf according to the current fitness function value of the individual gray wolf includes: and when the current fitness function value of the grey wolf individual is smaller than the fitness function value of the grey wolf individual before the mutation operation, updating the position of the grey wolf individual.

The invention has the beneficial effects that:

1. a double-ring control structure of a speed outer ring and a current inner ring is adopted, an active disturbance rejection controller is used for the outer ring, a PID (proportion integration differentiation) controller is used for the inner ring, and a permanent magnet synchronous motor control closed loop containing a first-order active disturbance rejection speed controller is adopted.

2. The improved gray wolf optimization algorithm is provided from three aspects of initializing a gray wolf population, designing a convergence factor which is nonlinearly changed along with the increase of iteration times and introducing a levy flight strategy in a wolf population position updating link according to a tent mapping reverse learning strategy, the diversity of the initial population of the algorithm can be improved, the adaptability and the adjustability to complex search are better, the local optimization can be avoided, the convergence speed and the global optimization capability of the algorithm are improved, and the effect is better than that of other improved gray wolf optimization algorithms.

3. The invention applies the improved wolf optimization algorithm to the parameter setting problem of the rotating speed ring active disturbance rejection controller, can obtain the optimal controller parameter, solves the problem that the parameter of the controller is difficult to set, and improves the rotating speed control precision, the responsiveness and the stability of the system.

Drawings

The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:

FIG. 1 is a schematic structural diagram of a control closed loop of a permanent magnet synchronous motor constructed in step 1 of the invention;

FIG. 2 is a flow chart of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, an embodiment of the present invention provides a method for setting parameters of an active disturbance rejection controller of a rotating speed loop of a permanent magnet synchronous motor, which is established on a closed control loop of the permanent magnet synchronous motor with a first-order active disturbance rejection speed controller, which is established by a speed outer loop and a current inner loop. During simulation, the convergence factor of the nonlinear change is repeatedly obtained in the iterative process, and the levy flight strategy is introduced for carrying out mutation operation in updating the position of the wolf individual, so that the capability of jumping out of local optimum of the algorithm is improved, and the possibility of parameter misconvergence is reduced.

As shown in fig. 2, an embodiment of the present invention provides a method for setting parameters of an active disturbance rejection controller of a rotating speed ring of a permanent magnet synchronous motor, including the following steps:

step 1: a permanent magnet synchronous motor control closed loop with a first-order active disturbance rejection speed controller is constructed by adopting a speed outer loop and a current inner loop, wherein the active disturbance rejection speed controller comprises: a first-order tracking differentiator (TD for short), a second-order extended state observer (ESO for short) and a state error feedback law (NLSEF for short).

TD includes a given speed signal ω^*Arranging the process to further obtain a fast overshoot-free tracking value and a differential signal of a given rotating speed required by the generation control law:

in the formula: omega₁Is the feedback speed of the system; omega^*A given speed for the system; r is an adjustable parameter; fal (e)₀,α₀,δ₀) The fast optimal control comprehensive function is a power function with linear continuity near an origin, and the general expression of the function is as follows:

in the formula: e is an error signal; alpha is an adjustable parameter; δ is the filter factor and sgn (e) is the sign function.

The ESO includes a tracking speed feedback value omega, an estimated value z of a state variable of the system₁And a real-time estimate z of the total disturbance of the system₂The specific expression is as follows:

in the formula: omega is the actual speed of the motor, z₁For state estimation of the actual rotational speed, z₂For an estimate of unknown disturbance, i_qIs the q-axis current of the motor, beta₀₁And beta₀₂Two adjustable parameters.

NLSEF is a tracking signal omega given by TD₁With the state variable estimate z obtained in ESO₁The error between the two is processed in a non-linear way to obtain a primary control quantity u₀And then the control action output obtained from the anti-interference controller through the disturbance compensation is as follows:

in the formula: beta is a₃Is an adjustable parameter.

Determining the parameter to be optimized as r, beta according to the process of designing the active disturbance rejection controller₀₁，β₀₂And beta₃。

Step 2: setting parameters for improving the gray wolf optimization algorithm, including setting the scale of a gray wolf population, the dimension of a search aperture of the gray wolf population, the maximum iteration times of the algorithm, parameters which do not need to be set in an active disturbance rejection controller and termination conditions;

and step 3: initializing a grey wolf population in an improved grey wolf optimization algorithm by adopting a tent mapping reverse learning strategy, sequentially taking a position vector of each grey wolf individual in the grey wolf population as a parameter to be set of an active disturbance rejection controller, simulating a permanent magnet synchronous motor control closed loop constructed in the step 1, and executing an iterative process from the step 4 to the step 7;

firstly, initializing a wolf population by Tent mapping, and generating a chaos sequence x ═ { x ] in a D-dimensional space_dD1, 2, D }, and generating a chaotic sequence X according to the formula X_min+x_t·(X_max-X_min) Mapping to the value range of the wolf population to obtain a wolf population X, wherein X_minAnd X_maxSearch upper and lower bounds for X.

Secondly, generating a reverse gray wolf population through reverse learning, namely performing reverse learning on a gray wolf population X according to a certain probability to obtain a reverse gray wolf population OX of the gray wolf population X, wherein the specific formula is as follows:

in the formula: p is the inverse learning probability.

Finally, combining the wolf grey population X and the reverse wolf grey population OX to obtain a new wolf grey population X_newX ∪ OX, and then X is calculated_newAnd selecting N grey wolf individuals with the best fitness function values as an initial grey wolf population.

And 4, step 4: calculating the fitness function value of each individual grey wolf according to the fitness function, sequencing the fitness function values of all the individual grey wolf, respectively marking the grey wolf individuals in the first three as alpha wolf, beta wolf and delta wolf, and marking the rest grey wolf individuals as omega wolf;

and 5: acquiring a convergence factor of nonlinear change according to the current iteration times, and then acquiring coefficient vectors A and C for updating the subsequent wolf position;

step 6: updating the omega wolf according to the positions of the alpha wolf, the beta wolf and the delta wolf and coefficient vectors A and C to obtain a new grey wolf population, then performing variation operation on all grey wolf individuals in the new grey wolf population by adopting a levy flight strategy, and updating the positions of the grey wolf individuals according to the current fitness function values of the grey wolf individuals;

firstly, updating self positions of other omega wolfs according to the positions of the alpha wolf, the beta wolf and the delta wolf and coefficient vectors A and C to obtain a new grey wolf population, then carrying out variation operation on the positions of individual grey wolfs by adopting a levy flight strategy, and updating the positions of the individual grey wolfs when the current fitness function value of the individual grey wolf is smaller than the fitness function value of the individual grey wolf before the variation operation. long-term short-distance search of the levy flight strategy can enable the wolfsbane population to continuously search for preys around the current optimal solution, and highlight local search capability; the occasional long-distance jump search of the levy flight strategy enlarges the optimizing range of the wolf population and improves the global search capability of the wolf population. The expression is as follows:

wherein r is₁And r₂Is [0,1 ]]The random number of (a) is set,

s is a random step size, u and v are respectively in [0,1 ]]The interval is normally distributed, and beta is 1.5.

And 7: and when the stopping condition is met, outputting the optimal parameters of the active disturbance rejection controller and the corresponding fitness function value, and stopping iteration.

The stopping condition is divided into two parts, and when the iteration times do not reach the maximum iteration times, the fitness function value is terminated if the fitness function value reaches a set range; or the iteration times reach the maximum iteration times, namely the termination. And finally, outputting the optimal parameters of the active disturbance rejection controller and the corresponding fitness function values, and applying the optimal parameters and the corresponding fitness function values to the setting of the parameters.

Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.