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Neural Network Filtering Control Design for Nontriangular Structure Switched Nonlinear Systems in Finite Time | IEEE Journals & Magazine | IEEE Xplore

Neural Network Filtering Control Design for Nontriangular Structure Switched Nonlinear Systems in Finite Time


Abstract:

This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitr...Show More

Abstract:

This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitrarily switchings, and the unknown functions which are constructed with the whole states. Neural network is used to simulate the uncertain systems and a filter-based state observer is designed to estimate the immeasurable states in this paper, respectively. Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented. Due to the developed finite-time control strategy, the closed-loop signals can be ensured to be bounded under arbitrarily switchings, and the outputs of systems can quickly track the desired reference signals in finite time. The effectiveness of the proposed method is given through its application to a mass-spring-damper system.
Page(s): 2153 - 2162
Date of Publication: 14 November 2018

ISSN Information:

PubMed ID: 30442617

Funding Agency:


I. Introduction

During the past few decades, lots of research studies [1]–[11] has been done to solve and improve the approximated-based control design problems of the strict-feedback systems with unknown dynamics. Among these works, the adaptive fuzzy/neural backstepping design technique is the core methods to solve these problems. The works in [1]–[4] investigated content focused on state-feedback-based adaptive fuzzy/neural control methods focus on either the single-input and single-output (SISO) or the multiple-input and multiple-output (MIMO) nonlinear systems with unknown dead zone and input nonlinearities, respectively. Due to some actual systems, the state of the system cannot be directly measured, then, Wang et al. [5] proposed an output-feedback-based neural model-based predictive control approach for the multirate networked industrial process control. Decentralized control is very important for the interconnected systems. With the unknown nonlinearities, a series of adaptive state/output-feedback decentralized control issues has been investigated for a type of uncertain nonlinear large-scale systems among research studies [6]–[8]. They extended from the SISO or MIMO system to the large-scale nonlinear systems. Later, researchers in [9]–[11] exploited the controlled plants in the systems including deterministic and uncertain large-scale nonlinear stochastic. Although the research results mentioned above have achieved excellent improvements and provided an important methodology to control those uncertain nonlinear systems, there exit three limited conditions. The first one is that the controlled plants of [1]–[11] did not consider the switched nonlinear systems; the second necessary factor is the consideration of the control schemes which are only suitable for the strict-feedback nonlinear systems instead of the nonstrict-feedback nonlinear systems. The considered control scheme is the third significant factor; there are all general infinite time control methods in their research studies without considering the finite-time performance of the controller.

References

References is not available for this document.