Adaptive Neural Output-Feedback Control of Uncertain Nonlinear Systems Based on Tuning Functions Approach

Abstract

Achieving the computational reduction is an interesting problem in adaptive neural control design. In this work, a new output feedback adaptive neural control method is proposed for uncertain nonlinear systems based on tuning functions approach. Different from most exiting works on backstepping-based output feedback control using neural networks, we obviate the utilization of neural networks at each step of backstepping design, but only one neural network is required at the first step. With the proposed approach, all the closed-loop signals are bounded and the tracking error ultimately converges a tunable residual. Simulation results validate the theoretical findings.