Limit-cycle analysis of dynamic fuzzy control systems

Perng, Jau-Woei

doi:10.1007/s00500-012-0971-9

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…DF approach is a well-known and practical tool in the frequency domain framework to predict the existence of limit cycle [19]. The stability analysis of FCSs has been successfully discussed based on the limit cycles prediction [24][25][26][27][28][29][30][31][32][33][34]. In these studies, integrating the parameter plane, stability equation, and DF methods provides a systematic procedure to analyze the stability of dynamic FCSs [26], uncertain fuzzy vehicle [27], and fuzzy vehicle lateral [30] control systems.…”

Section: Introductionmentioning

confidence: 99%

“…The stability analysis of FCSs has been successfully discussed based on the limit cycles prediction [24][25][26][27][28][29][30][31][32][33][34]. In these studies, integrating the parameter plane, stability equation, and DF methods provides a systematic procedure to analyze the stability of dynamic FCSs [26], uncertain fuzzy vehicle [27], and fuzzy vehicle lateral [30] control systems. Analysis of stability margins is a major concern for nonlinear control systems to test controller design.…”

Section: Introductionmentioning

confidence: 99%

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Stability analysis of dynamic interval type‐2 fuzzy control systems in frequency domain

Namadchian

Zare

2020

Asian Journal of Control

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The present paper aims to analyze the stability of dynamic Interval Type-2 fuzzy control systems (IT2 FCSs) with adjustable parameters by investigating the existence of limit cycles based on the describing function (DF) method. To simplify the stability analysis, the following structured procedure is described. First, to avoid complex computation, a simple architecture of the IT2 FCS using two embedded Type-1 fuzzy control systems (T1 FCSs) is proposed. Then, the DF of IT2 FCS is obtained based on the DFs of embedded T1FCSs. Subsequently, integrating the stability equation and parameter plane approaches provides a solution to identify the limit cycle and the asymptotically stability regions. Moreover, particle swarm optimization (PSO) technique is utilized to minimize the limit cycle region. Other focus of this paper is on the stability margins analysis for robust design. For this purpose, a gain-phase margin tester is applied to determine the minimum Gain Margin (GM min ) and Phase Margin (PM min ) when limit cycles can arise. Finally, two simulation examples are given to verify the effectiveness of the proposed approach.

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