阅读说明：本技术 一种基于间接迭代学习自适应mrac控制器的电机控制方法 (Motor control method based on indirect iterative learning self-adaptive MRAC controller ) 是由 史敬灼 徐丹旸 刘姝贝 刘悦琪 徐浩然 于 2021-08-11 设计创作，主要内容包括：本发明属于电机控制技术领域,具体涉及一种基于间接迭代学习自适应MRAC控制器的电机控制方法。本发明采用间接迭代学习自适应MRAC控制器对电机的转速进行闭环控制；其中,所述间接迭代学习自适应MRAC控制器包括MRAC控制器和迭代学习控制器；本发明根据所述当前迭代控制过程中迭代学习控制器的输出值、转速给定值、以及输出误差值,对当前迭代控制过程中MRAC控制器的前馈增益的自适应律进行调整。本发明响应速度明显加快且响应过程依然平稳,控制性能明显改善。相较于基于MRAC控制器的控制器方法的性能,本发明方法增加控制系统的自适应能力,且计算量小。(The invention belongs to the technical field of motor control, and particularly relates to a motor control method based on an indirect iterative learning self-adaptive MRAC controller. The invention adopts an indirect iterative learning self-adaptive MRAC controller to carry out closed-loop control on the rotating speed of the motor; wherein the indirect iterative learning adaptive MRAC controller comprises an MRAC controller and an iterative learning controller; the invention adjusts the self-adaptive law of the feedforward gain of the MRAC controller in the current iteration control process according to the output value, the rotating speed set value and the output error value of the iteration learning controller in the current iteration control process. The invention has the advantages of obviously accelerated response speed, stable response process and obviously improved control performance. Compared with the performance of the controller method based on the MRAC controller, the method provided by the invention has the advantages that the self-adaptive capacity of the control system is increased, and the calculated amount is small.)

1. A motor control method based on an indirect iterative learning adaptive MRAC controller is characterized by comprising the following steps:

carrying out closed-loop control on the rotating speed of the motor by adopting an indirect iterative learning self-adaptive MRAC controller;

wherein the indirect iterative learning adaptive MRAC controller comprises an MRAC controller and an iterative learning controller; the MRAC controller adjusts and controls the rotating speed of the motor in the current iteration control process; the iterative learning controller obtains an output value of the iterative learning controller in the current iterative control process according to the output value of the iterative learning controller in the previous iterative control process and an output error value in the previous iterative control process, wherein the output error value is a difference value between a reference model output value and a rotating speed actual value in the MRAC controller; and adjusting the self-adaptive law of the feedforward gain of the MRAC controller in the current iteration control process according to the output value, the rotating speed set value and the output error value of the iteration learning controller in the current iteration control process.

2. The method for controlling a motor based on an indirect iterative learning adaptive MRAC controller as claimed in claim 1, wherein the adaptive law of the feed forward gain of the adjusted MRAC controller is:

wherein k is₀Is a feed forward gain; r is the adaptive gain; e.g. of the type_k(i)、y_rk(i) And y_k(i) Respectively is the output error value, the given value of the rotating speed and the output value of the iterative learning controller at the moment i in the kth iterative control process, and e_k(i)＝y_mk(i)-y_k(i)，y_mk(i)、y_k(i) And respectively a reference model output value and a rotating speed actual value at the moment i in the kth iterative control process.

3. The method for controlling a motor based on an indirect iterative learning adaptive MRAC controller according to claim 1, wherein the adaptive law of the feedback gain of the MRAC controller is:

wherein d is the feedback gain; r is the adaptive gain; e.g. of the type_k(i) Is the output error value at time i in the kth iterative control process, and e_k(i)＝y_mk(i)-y_k(i)，y_mk(i)、y_k(i) And respectively a reference model output value and a rotating speed actual value at the moment i in the kth iterative control process.

4. The method for controlling a motor of an adaptive MRAC controller based on indirect iterative learning of claim 3, wherein the adaptive law of the control parameters of the MRAC controller is designed by selecting a Lyapunov function as:

in the formula, V is the selected Lyapunov function; p and gamma are positive definite symmetric matrixes; e.g. of the type_kIs an augmented error vector;is a parametric error vector.

5. The method for controlling the motor based on the indirect iterative learning adaptive MRAC controller according to any claim 1 to 4, wherein the motor is an ultrasonic motor.

Technical Field

The invention belongs to the technical field of motor control, and particularly relates to a motor control method based on an indirect iterative learning self-adaptive MRAC controller.

Background

The ultrasonic motor has a series of advantages of compact structure, easy miniaturization, fast response and braking, good control characteristic, high positioning precision, small noise and the like, and has wide application scenes and use values in the high and new technical fields of aviation, aerospace, medical treatment, precision instruments and the like.

Due to the special operation mechanism of the ultrasonic motor, the operation characteristic of the ultrasonic motor shows obvious nonlinear and time-varying characteristics, and ideal control performance is not easy to obtain. In an effort to overcome these disadvantages of the ultrasonic motor itself, and to obtain a control performance and operational stability that meet the application expectations, research into a control strategy thereof has gradually become complicated. Many complex controllers, such as neural network controllers, fuzzy neural network controllers, etc., have been proposed and applied to ultrasonic motors in sequence.

However, for reasons such as cost, debugging, and system maintenance, it is always desirable that the control system be simple in structure, and for these reasons, the model reference adaptive control strategy (MRAC) based on input and output variables proposed in 1978 by Narendra Kumpati s and valvani Lena s has been gradually applied to the ultrasonic motor control. The method has wide application range and mature design method, and is widely applied all the time.

However, it also presents a significant problem: in the control strategy, the value of the adaptive gain matrix gamma determines the adaptive adjustment rate of the controller parameter, and directly influences the control performance of the system. However, the control strategy does not provide a method for determining the Γ value, and usually a control system simulation method is adopted to try and correct the Γ value according to expected control performance, and finally a proper value is determined through field debugging. This is often the most time consuming step in applying the control strategy to a practical process and can also lead to undesirable control performance.

Disclosure of Invention

The invention provides a motor control method based on an indirect iterative learning self-adaptive MRAC controller, which is used for solving the problem of non-ideal control performance when the MRAC controller is used for controlling a motor.

In order to solve the technical problems, the technical scheme and the corresponding beneficial effects of the technical scheme are as follows:

the invention provides a motor control method based on an indirect iterative learning self-adaptive MRAC controller, which comprises the following steps:

carrying out closed-loop control on the rotating speed of the motor by adopting an indirect iterative learning self-adaptive MRAC controller;

wherein the indirect iterative learning adaptive MRAC controller comprises an MRAC controller and an iterative learning controller; the MRAC controller adjusts and controls the rotating speed of the motor in the current iteration control process; the iterative learning controller obtains an output value of the iterative learning controller in the current iterative control process according to the output value of the iterative learning controller in the previous iterative control process and an output error value in the previous iterative control process, wherein the output error value is a difference value between a reference model output value and a rotating speed actual value in the MRAC controller; and adjusting the self-adaptive law of the feedforward gain of the MRAC controller in the current iteration control process according to the output value, the rotating speed set value and the output error value of the iteration learning controller in the current iteration control process.

The beneficial effects of the above technical scheme are: the invention borrows the idea of iterative learning control, combines the iterative learning controller with the MRAC controller, utilizes the output of the iterative learning controller to directly act on the self-adaptive law of the feedforward gain, and can stabilize the system in an expected control state after several iterative calculations, thereby obviously accelerating the response speed, ensuring the response process to be stable and obviously improving the control performance. Compared with the performance of the controller method based on the MRAC controller, the method provided by the invention has the advantages that the self-adaptive capacity of the control system is increased, and the calculated amount is small.

Further, the adaptive law of the feed forward gain of the adjusted MRAC controller is:

wherein k is₀Is a feed forward gain; r is the adaptive gain; e.g. of the type_k(i)、y_rk(i) And y_k(i) Respectively is the output error value, the given value of the rotating speed and the output value of the iterative learning controller at the moment i in the kth iterative control process, and e_k(i)＝y_mk(i)-y_k(i)，y_mk(i)、y_k(i) And respectively a reference model output value and a rotating speed actual value at the moment i in the kth iterative control process.

Further, the adaptive law of the feedback gain of the MRAC controller is as follows:

wherein d is the feedback gain; r is the adaptive gain; e.g. of the type_k(i) Is the output error value at time i in the kth iterative control process, and e_k(i)＝y_mk(i)-y_k(i)，y_mk(i)、y_k(i) And respectively a reference model output value and a rotating speed actual value at the moment i in the kth iterative control process.

Further, a lyapunov function selected when the adaptive law of the control parameters of the MRAC controller is designed is:

in the formula, V is the selected Lyapunov function; p and gamma are positive definite symmetric matrixes; e.g. of the type_kIs an augmented error vector;is a parametric error vector.

Further, the motor is an ultrasonic motor.

Drawings

FIG. 1 is a control block diagram of a rotational speed closed-loop control system of an ultrasonic motor of the present invention;

FIG. 2(a) is a plot of the speed step response of the present invention;

FIG. 2(b) is a graph of the output of the iterative learning controller of the present invention;

FIG. 2(c) shows the feedforward gain k of the controller of the present invention₀A graph of the variation of the values;

fig. 3 is a graph comparing experimental results of the control method proposed by the invention and the control method based on the classical MRAC controller.

Detailed Description

The basic concept of the invention is as follows: the invention borrows the idea of iterative learning control, combines the iterative learning controller with the MRAC controller, utilizes the iterative learning controller to automatically adjust the self-adaptive gain of the MRAC controller on line, and replaces the obvious improvement of the MRAC controller performance with smaller design and realization complexity cost.

The following describes a motor control method based on an indirect iterative learning adaptive MRAC controller according to the present invention in detail with reference to the accompanying drawings and embodiments.

The method comprises the following steps:

in the embodiment of the motor control method based on the indirect iterative learning adaptive MRAC controller, a control block diagram of a rotating speed closed-loop control system of an ultrasonic motor, which is realized by combining an iterative learning controller and the MRAC controller, is shown in fig. 1, and the controller obtained by combining the iterative learning controller and the MRAC controller is referred to as the indirect iterative learning adaptive MRAC controller.

The content within the dashed-dotted line box in fig. 1 is a control block diagram for controlling the rotation speed of the ultrasonic motor by using a standard MRAC controller, and the ultrasonic motor and its driving circuit together constitute a controlled object of the MRAC controller. The iterative learning controller is used for adjusting and controlling the rotating speed of the motor in the current iterative control process. In FIG. 1, y_rk(i)、y_mk(i)、y_k(i)、e_k(i)、Δy_rk(i) Respectively is a given value of the rotating speed at the moment i, a reference model output value, an actual value of the rotating speed of the motor, an output error value and an output value of the iterative learning controller in the process of the kth iterative control, and comprises the following steps:

e_k(i)＝y_mk(i)-y_k(i) (1)

as previously mentioned, the portion within the dotted box in FIG. 1 is a standard baseMRAC speed controller for input and output variables. The controller includes a feed forward gain k₀And a feedback gain d₀Equal two adjustable parameters, using adaptive law pair k₀And d₀And (3) carrying out online adjustment, theoretically realizing complete matching of the reference model and the controlled motor, and enabling the response process of the motor rotating speed to be consistent with the output of the reference model so as to achieve the expected control performance.

The MRAC controller is first designed according to a classical design method.

Choosing a Lyapunov (Lyapunov) function as:

in the formula, P and gamma are positive definite symmetric matrixes; e.g. of the type_kIs an augmented error vector;is a parametric error vector.

The derived adaptive law for the control parameter θ is:

in the formula, e_k(i) The output error at the time point i in the k-th iteration control process is shown as an equation (1).

Taking gamma as the following positive definite diagonal matrix:

in the formula, the adaptive gain r is a positive real number.

Will be represented by the formulaθ^T＝[k₀d₀]Substitution of formula (4) gives k₀And d₀Is respectively as：

In the formula, e_k(i)、y_rk(i) And y_k(i) All can be measured, and only the numerical value of r needs to be specified, so that k can be realized by using the formula₀、d₀Adaptive adjustment of (3).

This completes the design of the MRAC controller and an iterative learning controller is described below.

In fig. 1, the output value Δ y of the iterative learning controller_rkWith given value y of speed_rkThe result of the addition is applied to the adaptation law (6). Namely: will feed forward gain k₀The adaptive law (6) is changed into the following steps:

and the iterative learning controller in fig. 1 is designed to:

Δy_rk(i)＝Δy_r(k-1)(i)+λ_Pe_(k-1)(i+1) (9)

in the formula, the coefficient λ_PA proportional learning gain; Δ y_r(k-1)(i) And e_(k-1)(i +1) is the output quantity of the iterative learning controller at the time i and the output error value (the difference between the reference model output value and the actual motor rotating speed value, which is calculated by the formula (1)) at the time i +1 in the k-1 th iterative control process respectively.

The design of the indirect iterative learning adaptive MRAC controller is completed. After the indirect iterative learning self-adaptive MRAC controller is designed, the designed indirect iterative learning self-adaptive MRAC controller can be adopted to carry out closed-loop control on the rotating speed of the ultrasonic motor, and the motor control method based on the indirect iterative learning self-adaptive MRAC controller is realized.

The method is applied in the following specific examples to illustrate the effectiveness of the method of the invention.

The control system shown in fig. 1 is implemented by programming a DSP chip to verify the validity of the iterative learning adaptive MIT controller described above. The motor for the experiment is a Shinsei USR60 type traveling wave ultrasonic motor, the driving circuit is of a two-phase H-bridge structure, and a phase shift PWM control mode is adopted.

Setting step given value y of rotating speed_rkThe rotation speed response curve is shown in fig. 2(a), and the change curve of the r value is shown in fig. 2 (b).

As can be seen from the experimental results shown in FIG. 2, the learning convergence rate is fast, the 5 th step response approaches the output of the reference model, and the subsequent 6 th step response curve substantially coincides with the 5 th step response curve and has stabilized in the desired control state. The output Δ y of the iterative learning controller shown in FIGS. 2(b), (c)_rkAnd a feedforward gain k₀The intermediate process of the system shown in fig. 1 for gradually improving the control performance through iterative learning is shown. FIG. 2(b) shows that as the iterative learning process progresses, Δ y_rkIs increasingly large. As can be seen from equation (8), Δ y is increasingly larger_rkResulting in a feed forward gain k₀The adjustment rate of (a) is increased so that k is₀The rate of rise in the step response dynamic process is increased as shown in fig. 2 (c). This increases the amount of control, and the control action is enhanced, thus obtaining a faster and faster response speed as shown in fig. 2 (a).

Fig. 3 compares the control performance of the proposed controller with that of a classical MRAC controller, and it can be seen that with the proposed controller, the response speed is significantly increased, the response process is still stable, and the control performance is significantly improved.

Therefore, the control method is effective, can effectively improve the performance of the MRAC control system by utilizing the learning capability of the iterative learning controller, increases the self-adaptive capability of the control system, and has small calculation amount.

In this embodiment, the controlled object controls the rotation speed of the ultrasonic motor by using an indirect iterative learning adaptive MRAC controller designed for the ultrasonic motor and a driving circuit thereof. As other implementation modes, the indirect iterative learning self-adaptive MRAC controller designed by the invention is also suitable for other types of motors and correspondingly controls the rotating speeds of the other types of motors.