Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Chen, Bor‐Sen; Ho, S. Y.

doi:10.1016/j.fss.2015.06.014

Cited by 45 publications

(26 citation statements)

References 43 publications

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“…For the multiobjective H 2 / H ∞ filtering design of the stochastic T‐S fuzzy system, Chen et al developed an LMI‐based multiobjective evolution algorithm to derive Pareto optimal solutions. In order to achieve simultaneous optimization of H 2 / H ∞ tracking objectives, Chen and Ho used the LMI‐based multiobjective evolution algorithm to efficiently search the set of Pareto optimal solutions for the stochastic T‐S fuzzy systems. Zhang et al considered the LQ Pareto optimal control problem of stochastic singular systems in finite horizon.…”

Section: Introductionmentioning

confidence: 99%

Necessary/sufficient conditions for Pareto optimum in cooperative difference game

Lin

Zhang

2017

Optim Control Appl Methods

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Summary This paper is concerned with the necessary/sufficient conditions for the Pareto optimum of the cooperative difference game in finite horizon. Utilizing the necessary and sufficient characterization of the Pareto optimum, the problem is transformed into a set of constrained optimal control problems with a special structure. Employing the discrete version of Pontryagin's maximum principle, the necessary conditions for the existence of the Pareto solutions are derived. Under certain convex assumptions, it is shown that the necessary conditions are sufficient too. Next, the obtained results are extended to the linear‐quadratic case. For a fixed initial state, the necessary conditions resulting from the maximum principle and the convexity condition on the cost functional provide the necessary and sufficient description of the well‐posedness of the weighted sum optimal control problem. For an arbitrary initial state, the solvability of the related difference Riccati equation provides a sufficient condition under which the Pareto‐efficient strategies are equivalent to the weighted sum optimal controls. In addition, all Pareto solutions are derived based on the solutions of a set of difference equations. Two examples show the effectiveness of the proposed results.

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Section: Introductionmentioning

confidence: 99%

Necessary/sufficient conditions for Pareto optimum in cooperative difference game

Lin

Zhang

2017

Optim Control Appl Methods

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“…To enable the handling of such uncertainty, recent studies on intelligent control suggested the direct incorporation of human expertise into neural networks [7], [9]. Fuzzy inference systems have been employed as the adaptive controllers for robots [10]- [14], showing one of the most successful applications of fuzzylogic systems [15]- [20]. Naturally, the neural networks have been fuzzified in various ways to address the presence of uncertainty [7], [21], [22], with a number of successful applications in uncertain environments [23].…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Chao

Zhou

Lin

et al. 2020

IEEE Trans. Cybern.

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“…In the recent years, the research on T-S fuzzy system has been deeply researched, and a lot of significant results have been obtained, see [8, 9, 15, 16, 22-25, 37, 41] and the references therein. In particular, multiobjective optimization problem of T-S fuzzy systems was investigated in [1,28] recently. It is worth mentioning that in many industrial applications, inaccuracy will inevitably occur in the implementation of the controller due to numerical rounding errors and actuator degradation, which leads to the study of nonfragile controllers [33].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective nonfragile fuzzy control for nonlinear stochastic financial systems with mixed time delays

Yang

Xia

Zhao

et al. 2019

NAMC

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In this study, a multiobjective nonfragile control is proposed for a class of stochastic Takagi and Sugeno (T–S) fuzzy systems with mixed time delays to guarantee the optimal H2 and H∞ performance simultaneously. Firstly, based on the T–S fuzzy model, two form of nonfragile state feedback controllers are designed to stabilize the T–S fuzzy system, that is to say, nonfragile state feedback controllers minimize the H2 and H∞ performance simultaneously. Then, by applying T–S fuzzy approach, the multiobjective H2/H∞ nonfragile fuzzy control problem is transformed into linear matrix inequality (LMI)-constrained multiobjective problem (MOP). In addition, we efficiently solve Pareto optimal solutions for the MOP by employing LMI-based multiobjective evolution algorithm (MOEA). Finally, the validity of this approach is illustrated by a realistic design example.

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Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Cited by 45 publications

References 43 publications

Necessary/sufficient conditions for Pareto optimum in cooperative difference game

Necessary/sufficient conditions for Pareto optimum in cooperative difference game

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Multiobjective nonfragile fuzzy control for nonlinear stochastic financial systems with mixed time delays

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