Output stabilization of Takagi–Sugeno fuzzy systems

Yoneyama, Jun; Nishikawa, Masahiro; Katayama, Hitoshi; Ichikawa, Akira

doi:10.1016/s0165-0114(98)00121-3

Cited by 161 publications

(76 citation statements)

References 6 publications

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“…and the feedback gains as G 1 = G 11 , G 2 = G 12 , G 3 = G 21 , and G 4 = G 22 . Under such a design, the sampled-data fuzzy controller of (6) with p = Ω = 2 can be written in the form of (9)…”

Section: B Sampled-data Fuzzy Controllermentioning

confidence: 99%

Sampled-Data Fuzzy Controller for Time-Delay Nonlinear Systems: Fuzzy-Model-Based LMI Approach

Lam

Leung

2007

IEEE Trans. Syst., Man, Cybern. B

141

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Abstract-This paper presents the stability analysis and performance design for a sampled-data fuzzy control system with time delay, which is formed by a nonlinear plant with time delay and a sampled-data fuzzy controller connected in a closed loop. As the sampled-data fuzzy controller can be implemented by a microcontroller or a digital computer, the implementation time and cost can be reduced. However, the sampling activity and time delay, which are potential causes of system instability, will complicate the system dynamics and make the stability analysis much more difficult than that for a pure continuous-time fuzzy control system. In this paper, a sampled-data fuzzy controller with enhanced nonlinearity compensation ability is proposed. Based on the fuzzy-model-based control approach, linear matrix inequality (LMI)-based stability conditions are derived to guarantee the system stability. By using a descriptor representation, the complexity of the sampled-data fuzzy control system with time delay can be reduced to ease the stability analysis, which effectively leads to a smaller number of LMI-stability conditions. Information of the membership functions of both the fuzzy plant model and fuzzy controller are considered, which allows arbitrary matrices to be introduced, to ease the satisfaction of the stability conditions. An application example will be given to show the merits and design procedure of the proposed approach. Furthermore, LMI-based performance conditions are derived to aid the design of a wellperformed sampled-data fuzzy controller.

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Section: B Sampled-data Fuzzy Controllermentioning

confidence: 99%

Sampled-Data Fuzzy Controller for Time-Delay Nonlinear Systems: Fuzzy-Model-Based LMI Approach

Lam

Leung

2007

IEEE Trans. Syst., Man, Cybern. B

141

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“…The results on output feedback control design for fuzzy systems have been obtained(for example, [8]- [12]). The papers [8] and [9] proposed an output feedback controller based on the separation principle and the standard PDC. The paper [10] considered the case of immeasurable premise variables.…”

Section: Introductionmentioning

confidence: 99%

Non-Fragile Dynamic Output Feedback Control Design for Fuzzy Stochastic Systems

Yoneyama

Hoshino

2016

Transactions of ISCIE

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The paper considers a non-fagile control design via dynamic output feedback controller for fuzzy stochastic systems that are subject to stochastic disturbances. In practical situations, robust control design methods should take case of disturbances into the system and variations in control gains, and they should assume that not all the states of the system are available for the feedback control. In addition, malfunction may occur in actuators. Hence, a non-fragile output feedback control design is desired. In this paper, robust stabilization via dynamic output feedback control with uncertain control gain for fuzzy stochastic systems is considered and a control design of robust stabilizing controllers is proposed. The robust stability analysis of the closed-loop system and non-fragile controller design via descriptor system approach are given in terms of LMI conditions. The resulting conditions are less conservative than the existing results. An illustrative example is provided to show the effectiveness of the proposed method.

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“…Since the stability analysis and state feedback stabilization first made in (Tanaka & Sugeno, 1992), system theory and various control schemes for fuzzy systems have been developed. Parallel to state feedback control design, observer problems were also considered in (Tanaka & Sano, 1994;Tanaka & Wang, 1997;Yoneyama et al, 2000;Yoneyama et al, 2001a). When the observer is available, it is known in (Yoneyama et al, 2000) that for fuzzy systems, the separation property of designing state feedback controller and observer is established.…”

Section: Introductionmentioning

confidence: 99%

“…Parallel to state feedback control design, observer problems were also considered in (Tanaka & Sano, 1994;Tanaka & Wang, 1997;Yoneyama et al, 2000;Yoneyama et al, 2001a). When the observer is available, it is known in (Yoneyama et al, 2000) that for fuzzy systems, the separation property of designing state feedback controller and observer is established. Thus, an output feedback controller for fuzzy systems was proposed in (Yoneyama et al, 2001a).…”

Section: Introductionmentioning

confidence: 99%