Abstract
Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The method includes three control substructures: fuzzy inference block, neural network control block and basic control block. The fuzzy inference block adaptively adjusts the neural network compensation control quantity according to the control error and the error rate of change, and adds a dynamic adjustment factor to ensure the control quality at the initial stage of network learning or at the moment of signal transition. The neural network control block is composed of an identifier and a controller with the same network structure. After the identifier learns the dynamic inverse model of the controlled object online, its training parameters are dynamically copied to the controller for real-time compensation control. The basic control block uses a traditional PID controller to provide online learning samples for the neural network control block. The simulation and experimental results of the position control of the magnetic levitation ball show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy of neural network compensation control, and has good transient and steady-state performance and strong robustness simultaneously.
Highlights
Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper
The main innovations of the proposed method are as follows: (1) A magnetic levitation ball position control structure based on fuzzy inference to adaptively adjust neural network control is proposed, which improves the transient performance of the control system; (2) The fuzzy inference block is designed to adaptively adjust the compensation control quantity of the neural network controller, and the dynamic adjustment factor is added to enhance the stability of the control; (3) Simulation and experimental results show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy
In order to further verify the effectiveness of the proposed method in real-time control, this paper conducts an experimental study based on the magnetic levitation ball position control experimental platform
Summary
Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The neural network has adaptive and self-learning c apabilities[12,13,14], can accurately approximate nonlinear systems[15] It can directly estimate the unknown nonlinear function without prior information of the closed-loop system[16], and can improve the position control accuracy of the magnetic levitation b all[17]. Chen et al proposed a sliding mode control method based on radial basis function (RBF) neural network, which significantly improved the control accuracy and robustness of the magnetic levitation ball control system[18]. Zhu et al used neural network identifier to establish the dynamic model between control system error and control quantity in the control loop, and dynamically copied its training parameters to the neural network feedback compensation controller, which significantly improved the steady-state accuracy of magnetic levitation ball position control[22]. The moment of signal transition to suppress the uncertainty interference of the undertrained neural network to the control system
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