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Introduction

  • Shanling DongEmail author
  • Zheng-Guang Wu
  • Peng Shi
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 268)

Abstract

It is well known that nonlinearity exists widely in practical industrial systems. To deal with it, various efficient approaches have been put forward, including the sliding mode control (SMC) law, the Lipschitz continuity technique and the smoothness approach. The T–S fuzzy model has been recognized as one of powerful and efficient tools in approximating nonlinear systems by dividing the original system into a family of linear subsystems with fuzzy rules and blending all subsystems with membership functions. It has been utilized extensively in many systems, such as networked control systems, manufacturing processes, chemical processes and robotic systems. Considerable attention has been paid to the analysis and synthesis of T–S fuzzy systems, for instance, the filtering design [1, 2, 3], the robust control [4, 5, 6], the dissipativity issue and the model approximation problem [7]. The T–S fuzzy model has been employed in [8] to investigate the adaptive finite-time stabilization issue for nonlinear systems with uncertain parameters. Via T–S fuzzy knowledge, the work in [9] has accurately modelled the nonlinear stochastic jump diffusion financial system for simplifying the investment policy. The event-triggered control issue has been studied in [10] for networked T–S fuzzy systems.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Laboratory of Industrial Control TechnologyInstitute of Cyber-Systems and Control, Zhejiang UniversityHangzhouChina
  2. 2.School of Electrical and Electronic EngineeringUniversity of AdelaideAdelaideAustralia
  3. 3.Victoria UniversityMelbourneAustralia

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