Adaptive neural decentralized control for nonlinear large-scale systems

Abstract

This paper presents an adaptive decentralized control method for a class of nonlinear large-scale systems with unknown nonlinear functions and bounded time varying state delays. The adaptive compensation controller is constructed by utilizing Neural Networks (NN) and a backstepping design method. With the help of NNs to approximate the unknown nonlinear functions, the novel adaptive control approach is developed by using the backstepping design method. The appropriate Lyapunov-Krasovskii type functionals are introduced to design new adaptive laws to compensate the unknown nonlinearities as well as uncertainties from unknown state delays. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. As an application, the proposed approach is employed for a two inverted pendulums. The simulation results show effectiveness of the proposed adaptive decentralized control approach.