Fuzzy control based on quadratic performance function-a linear matrix inequality approach

Tanaka, Kazuo; Taniguchi, T.; Wang, H.O.

doi:10.1109/cdc.1998.757921

Cited by 32 publications

(43 citation statements)

References 17 publications

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“…The representation relation (3) and (4) is often called a polytopic system [7] a class of parameter-dependent systems, which lends itself easily to practical computations. At first glance, it could appear that the additionally restricted structure (8) for PDC fuzzy control incurs conservatism in the synthesis problem in comparison with the most general structure (7) often considered in gain-scheduling control [14], [15]. In fact, the main contribution of [14] and [15] is to adapt the approach of [7] (for polytopic systems) to design PDC controller (8).…”

Section: Introductionmentioning

confidence: 98%

Parameterized linear matrix inequality techniques in fuzzy control system design

Tuan

Apkarian

Narikiyo

et al. 2001

IEEE Trans. Fuzzy Syst.

1,045

358

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This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations. Index Terms-Fuzzy systems, parameterized linear matrix inequality (PLMI).

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Section: Introductionmentioning

confidence: 98%

Parameterized linear matrix inequality techniques in fuzzy control system design

Tuan

Apkarian

Narikiyo

et al. 2001

IEEE Trans. Fuzzy Syst.

1,045

358

View full text Add to dashboard Cite

show abstract

“…Substituting (14) into (9) and (10), we obtain the closed loop systeṁ (15) and the performance criterion…”

Section: Optimal Fuzzy Controllermentioning

confidence: 99%

“…Tanaka and co-workers [14,15] tried to obtain a fuzzy controller to minimize the upper bound of the quadratic performance function by linear-matrix-inequality (LMI) approach based on the assumption of local-linear-feedback-gain control structure. Nevertheless, no theoretical analysis on this design scheme of optimal-fuzzy-control structure was proposed [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy control to reduce energy consumption in distillation columns

Bouyahiaoui

Grigoriev

Laaouad

et al. 2005

Autom Remote Control

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Distillation columns constitute a significant fraction of the capital invested in the refineries around the world; their control requires a major part of the total operating cost of chemical processes, if the used strategy is not adequate. This article presents the application of optimal fuzzy control to reduce the energy consumption of a Benzene-Toluene distillation column. This method is based on the determination of the specific values of the fuzzy controller parameters such that certain performance criterion is minimised. Results of a simulation study are presented showing the potential improvement offered by this method.

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“…T-S fuzzy systems and PDC were expanded by generalization of linear control system theory. The work was expanded to developing T-S fuzzy observers and regulators [12], robust control [13,14], optimal control [14,15,21] constraints on the input and output [16], and T-S control of nonlinear time-delayed systems [17]. Some performance criteria such as disturbance rejection [16], decay rate [18], and pole placement [19] have been incorporated in T-S fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Takagi-Sugeno Fuzzy Model-Based Control of Spacecraft with Flexible Appendage

Ayoubi

Sendi

2014

J of Astronaut Sci

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This paper presents a Takagi-Sugeno (T-S) fuzzy model-based approach to model and control a rigid spacecraft with flexible antenna. First, the equations of motion of the flexible spacecraft, which are based on Lagrange equations and given in terms of quasi-coordinates and the Rayleigh-Ritz method, are briefly reviewed. Then, the T-S fuzzy modeling and the parallel distributed compensation control technique are introduced. We utilize full state-feedback and optimal H ∞ robustness performance via a T-S fuzzy model to achieve position and attitude stabilization, vibration suppression, and disturbance rejection objectives. Finally, this technique is applied to the flexible spacecraft equations of motion resulting in a nonlinear controller. The controller produces an asymptotically stable closed-loop system which is robust to external disturbances and has a simple structure for straightforward implementation. Numerical simulation is provided for performance evaluation of the proposed controller design.

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Fuzzy control based on quadratic performance function-a linear matrix inequality approach

Cited by 32 publications

References 17 publications

Parameterized linear matrix inequality techniques in fuzzy control system design

Parameterized linear matrix inequality techniques in fuzzy control system design

Optimal fuzzy control to reduce energy consumption in distillation columns

Takagi-Sugeno Fuzzy Model-Based Control of Spacecraft with Flexible Appendage

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