CONSTRUCTION OF SWITCH NONLINEAR DYNAMIC SYSTEM USING ADAPTIVE NEURAL NETWORK TECHNOLOGY

Tingyan Xing, Adil Omar Khadidos () and Lin Li
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Tingyan Xing: School of Information Engineering, China University of Geosciences, Beijing, P. R. China
Adil Omar Khadidos: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Lin Li: School of Information Engineering, China University of Geosciences, Beijing, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-11

Abstract: In order to improve the intelligent tracking efficiency of switched nonlinear dynamic system, first, the optimized backpropagation neural network (BPNN) is proposed and the switched nonlinear dynamic system is constructed. Then, the optimized BPNN is extended and the adaptive control is studied. Finally, the extended neural network system related to the error is formed, and then the simulation analysis is carried out. The results show that under different reference signal conditions, the output peak values of the system are 1.7 and 1.5 at 2s, respectively, while the response peak value of the reference signal is 1. However, after 4s, the peak values of the two signals are very close; under different reference signals, the scaling factor decreases with the increase of time. After 6s, the value of scaling factor is 0. Under the condition of reference signal 1, the estimated values of parameters decrease with the increase of time, and tend to 0 after 6s. However, under the condition of reference signal 2, the estimated values of parameters in the first 6s increase with the increase of time; the peak values of the state variable x2 and the adaptive parameters in the system are in a stable state, while the peak values of the system controller and the system tracking error gradually decrease with the increase of time, and finally tend to a stable value. It suggests that the technique proposed in this work can guarantee the signal in switched nonlinear dynamic system to be bounded.

Keywords: Backpropagation Neural Network; Adaptive Control; Switched Nonlinear Dynamic System; Boundedness (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22400710

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