Neural network-based adaptive tracking control for switched nonlinear systems with prescribed performance: An average dwell time switching approach

This article investigates a problem of adaptive neural output-feedback tracking control for a class of switched uncertain nonlinear systems in nonstrict-feedback structure with average dwell time. For the system under study, many factors are taken into consideration, such as unknown nonlinearities, unmeasurable states, external disturbance, unknown dead-zone input, and prescribed performance bounds. A switched NN state observer is established to observe the unmeasurable states and alleviate the conservativeness induced by taking advantage of a common observer. In order to defeat the trouble originated from the nonstrict-feedback structure, an effective adaptive law is introduced by adopting the properties of NNs. The influence of dead-zone on control performance is restricted by designing a special adaptive law in the last step of the backstepping design frame. The stability of the closed-loop system is proved by average dwell time approach and Lyapunov stability theory. By utilizing the multiple Lyapunov function method and the backstepping technique together with the prescribed performance bounds, an adaptive NN controller is established which can ensure that all the signals in the closed-loop system are bounded under a class of switching signals with average dwell time and the tracking error converges to the predefined bounds. The feasibility of the presented control scheme is illustrated by the simulation results. ? 2021 Elsevier B.V. All rights reserved.

Automated summary

This article investigates adaptive neural output-feedback tracking control for a class of switched uncertain nonlinear systems with average dwell time. By utilizing techniques such as neural networks and Lyapunov stability theory, the stability of the closed-loop system is proven, and a controller that can control system signals and ensure convergence of tracking error is proposed.