Fuzzy Tracking Control of Fuzzy Linear Dynamical Systems for a fixed reference input under granular derivative

Abbasi, Seyed Mohammad Mehdi; Jalali, Ahvand

doi:10.52547/joc.15.1.149

Cited by 1 publication

(2 citation statements)

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“…Additionally, it should be noted that in comparison with [42] and [45] , the proposed fuzzy control structure illustrated in Fig. 3 is the more general case.…”

Section: Discussionmentioning

confidence: 99%

“…3 is the more general case. Specifically, the fuzzy control system presented in [42] is unable to reject the disturbance and lacks fuzzy observer, moreover, the controller designed in [45] lacks the fuzzy dynamic compensator and it is not able to reject the disturbance. Nevertheless, the proposed fuzzy controller in this work possesses disturbance rejection feature, fuzzy dynamic compensator provided by the granular fuzzy integral, and it has been equipped with the fuzzy observer in order to estimate uncertain system sates.…”

Section: Discussionmentioning

confidence: 99%

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Tracking Control of a Class of Fuzzy Dynamical Systems under the Concept of Granular Differentiability

Abbasi¹,

Jalali²

2023

Fuzzy Information and Engineering

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In this work, the tracking control of a class of uncertain linear dynamical systems is investigated. The uncertainty is considered to be represented as fuzzy numbers, and hence, these uncertain dynamical systems are referred to as fuzzy linear dynamical systems, which are presented in the form of fuzzy differential equations (FDEs). The solution of an FDE is found using an approach called relative-distance-measure fuzzy interval arithmetic and under the granular differentiability concept. The control objective is to provide a control law such that the output of the system tracks a desired reference input in the presence of uncertainties. To this end, a theorem is proposed, which suggests that the control law should take the form of a feedback of fuzzy states with fuzzy gains and a fuzzy pre-compensator. However, since the fuzzy states of the system may not always be measurable, a fuzzy observer is designed for the estimation of such fuzzy states. It is also clearly shown that the generalized Hukuhara differentiability concept is unable for solving the problem examined in this study. Finally, the efficiency of the approach is examined for a plane landing control problem.

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“…Additionally, it should be noted that in comparison with [42] and [45] , the proposed fuzzy control structure illustrated in Fig. 3 is the more general case.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%