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Antidisturbance Control Design for Interval Type-2 Fuzzy Stochastic Systems With Input Quantization

Ramasamy Kavikumar, Oh‐Min Kwon, B. Kaviarasan, R. Sakthivel

2022IEEE Transactions on Fuzzy Systems15 citationsDOI

Abstract

Quantized antidisturbance control design problem is presented for a class of interval type-2 (IT2) fuzzy stochastic systems subject to time delays and multiple disturbances. The inherent uncertain nonlinear and hybrid characteristics of the concerned system make it difficult to design a stable antidisturbance controller. In order to properly reflect the characteristics of IT2 fuzzy stochastic models with multiple disturbances, a new fuzzy disturbance observer is proposed to estimate the disturbances generated by a fuzzy exogenous system. Furthermore, a quantized fuzzy antidisturbance control scheme is synthesized by fusing the estimation of the multiple disturbances. With the support of Lyapunov functional method, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$It{\hat{o}}$</tex-math></inline-formula> ’s formula and auxiliary function-based integral inequality, the resulting IT2 fuzzy stochastic systems are proved to be robustly stochastically stable with mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> /passivity performance index. The design methods of the fuzzy disturbance observer and quantized antidisturbance controller are formulated in the form of linear matrix inequalities so that the corresponding gain matrices can be easily obtained. Finally, simulation studies on three examples are provided to justify the efficiency of the designed control strategy.

Topics & Concepts

Fuzzy control systemControl theory (sociology)Quantization (signal processing)Fuzzy logicFuzzy setInterval (graph theory)Computer scienceMathematicsControl (management)AlgorithmArtificial intelligenceCombinatoricsFuzzy Logic and Control SystemsStability and Control of Uncertain SystemsNeural Networks and Applications