Robust Adaptive Neural Network Control of Time-Varying State Constrained Nonlinear Systems

TL;DR

This paper introduces a robust adaptive neural network control method for time-varying nonlinear systems with state constraints, utilizing backstepping, Barrier Lyapunov Functions, and disturbance observers to ensure stability and constraint adherence.

Contribution

It presents a novel adaptive control strategy combining neural networks, barrier functions, and disturbance observers for constrained nonlinear systems.

Findings

Effective constraint handling demonstrated in simulations

Robustness against disturbances validated

Neural network approximation improves control accuracy

Abstract

This paper deals with the tracking control problem for a very simple class of unknown nonlinear systems. In this paper, we presents a design strategy for tracking control of time-varying state constrained nonlinear systems in an adaptive framework. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Function (BLF) is used so that the state variables do not contravene its constraints. In order to cope with the unknown dynamics of the system, an online approximator is designed using a neural network with a novel adaptive law for its weight update. To make the controller robust and computationally inexpensive, a disturbance observer is proposed to cope with the disturbance along with neural network approximation error and the time derivative of virtual control input. The effectiveness of the proposed approach is demonstrated through a simulation study.