Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems

In this paper, a local tracking control (LTC) scheme is developed via particle swarm optimized neural networks (PSONN) for unknown nonlinear interconnected systems. With the local input-output data, a local neural network identifier is constructed to approximate the local input gain matrix and the mismatched interconnection, which are utilized to derive the LTC. To solve the local Hamilton-Jacobi-Bellman equation, a local critic NN is established to estimate the proper local value function, which reflects the mismatched interconnection. The weight vector of the local critic NN is trained online by particle swarm optimization, thus the success rate of system execution is increased. The stability of the closed-loop unknown nonlinear interconnected system is guaranteed to be uniformly ultimately bounded through Lyapunov's direct method. Simulation results of two examples demonstrate the effectiveness of the developed PSONN-based LTC scheme. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper develops a local tracking control scheme for unknown nonlinear interconnected systems using particle swarm optimized neural networks (PSONN), which increases the success rate of system execution. The effectiveness of the developed PSONN-based LTC scheme is demonstrated through simulation results of two examples.