Original scientific paper
https://doi.org/10.1080/00051144.2019.1668139
Research on RBF neural network model reference adaptive control system based on nonlinear U – model
Fengxia Xu
; College of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, People’s Republic of China
Shanshan Wang
; College of Computer and Control Engineering, Qiqihar University, Qiqihar, People’s Republic of China
Furong Liu
; State Grid Heilongjiang Electric Power Co., Ltd, Qiqihar, People’s Republic of China
Abstract
The overall objective of this study is to design the nonlinear U-model-based radial basis function neural network model reference adaptive control system, through research into a class of complex time-varying nonlinear plants. First, the ideal nonlinear plant is adopted as the reference model and transformed into the U-model representation. In the process, the authors establish the corresponding relationship between the degrees of the reference nonlinear model and the controlled nonlinear plants, and carry out research into the corresponding coefficient relationship between the reference nonlinear model and the controlled nonlinear plants. Also, the impact of the adjusting amplitude and tracking speed of the model on the system control accuracy is analyzed. Then, according to the learning error index of the neural network, the paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety. With the weight coefficients and network parameters automatically updated and the adaptive controller adjusted, the output of controlled nonlinear plants can track the ideal output completely. The simulation results show that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive control system based on the gradient descent method.
Keywords
RBF neural network; nonlinear U-model; model reference adaptive
Hrčak ID:
239849
URI
Publication date:
3.12.2019.
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