Research on parallel nonlinear control system of PD and RBF neural network based on U model

• Published in: Automatika Vol. 61; no. 2; pp. 284 - 294
• Main Authors: Xu, Fengxia, Tang, Deqiang, Wang, Shanshan
• Format: Journal Article Paper
• Language: English
• Published: Ljubljana Taylor & Francis 02.04.2020; Taylor & Francis Ltd; KoREMA - Hrvatsko društvo za komunikacije,računarstvo, elektroniku, mjerenja i automatiku; Taylor & Francis Group
• Subjects: Computer simulation; Control algorithms; Control systems; Control theory; Iterative algorithms; Iterative methods; Machine learning; Modelling; Neural networks; Newton iteration; Nonlinear control; Nonlinear systems; Nonlinear U-model system; RBF neural networks
• ISSN: 0005-1144; 1848-3380; 1848-3380
• DOI: 10.1080/00051144.2020.1731227

The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system.