Abstract

In this paper, we investigate the stability of Takagi-Sugeno fuzzy-model-based (FMB) functional observer-control system. When system states are not measurable for state-feedback control, a fuzzy functional observer is designed to directly estimate the control input instead of the system states. Although the fuzzy functional observer can reduce the order of the observer, it leads to a number of observer gains to be determined. Therefore, a new form of fuzzy functional observer is proposed to facilitate the stability analysis such that the observer gains can be numerically obtained and the stability can be guaranteed simultaneously. The proposed form is also in favor of applying separation principle to separately design the fuzzy controller and the fuzzy functional observer. To design the fuzzy controller with the consideration of system stability, higher order derivatives of Lyapunov function (HODLF) are employed to reduce the conservativeness of stability conditions. The HODLF generalizes the commonly used first-order derivative. By exploiting the properties of membership functions and the dynamics of the FMB control system, convex and relaxed stability conditions can be derived. Simulation examples are provided to show the relaxation of the proposed stability conditions and the feasibility of designed fuzzy functional observer-controller.

Highlights

  • S TABILITY of nonlinear systems is complex and difficult to be systematically analyzed

  • The applicability of FMB control scheme has been improved by relaxing stability conditions and considering unmeasurable system states

  • The fuzzy controller has been designed via higher order derivatives of Lyapunov function (HODLF) to obtain relaxed stability conditions

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Summary

Introduction

S TABILITY of nonlinear systems is complex and difficult to be systematically analyzed. Fuzzy-model-based (FMB) control scheme [1] has been proposed as an efficient approach to conduct stability analysis and control synthesis for nonlinear systems. The nonlinear systems can be separated to several linear subsystems which are smoothly combined by membership functions. In this way, linear control techniques such as state-feedback control can be applied and extended to Manuscript received September 16, 2015; revised February 5, 2016; accepted April 6, 2016. Open access for this article was funded by King’s College London.

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