Abstract: A time-varying RBF neural network with time-varying properties is firstly proposed, and its approximation theorem is obtained. For a class of nonlinear systems with non-parametric time-varying uncertainties, the proposed time-varying RBF neural network is used to approximate the time-varying uncertainties, and the controller is designed by making use of Lyapunov stability theory and adaptive iterative learning control techniques. We obtain the stability theorem of the designed controller. The simulation results verify the effectiveness of the time-varying neural network and the correctness of the controller design scheme.
Li, Jing; Zhang, Taotao; Jin, Kai; Yuan, Shengzhi; and Zha, Zilong
"Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems,"
Journal of System Simulation: Vol. 35:
10, Article 14.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/14
Li Jing, Zhang Taotao, Jin Kai, et al. Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems[J]. Journal of System Simulation, 2023, 35(10): 2223-2236.
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