Fuzzy model-reference adaptive control

Yin, Tang-Kai; Lee, C.S.G.

doi:10.1109/cdc.1994.411594

Cited by 15 publications

(20 citation statements)

References 6 publications

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“…Corollary 3.1: If , then we can conclude that the following performance is achieved [2] for all (33) where denotes the weighting matrix and the constant is the attenuation level. If, in addition, , then we can conclude that and Proof: From (27) and by completing the squares, we get (34) for some weighting matrix and attenuation level .…”

Section: B Controller Design and Stability Analysis For Holonomic Comentioning

confidence: 94%

“…It should be noted that can be arbitrarily pre-assigned and can be arbitrarily small by increasing the gain . Integrating the above equation from to yields Since , the above inequality leads to (33). That is, the performance is achieved.…”

Section: B Controller Design and Stability Analysis For Holonomic Comentioning

confidence: 96%

“…Fuzzy control has been extensively applied to a wide variety of industrial systems and consumer products and has attracted the attention of several control researchers due to its computational simplicity and model free approach [8], [12], [14], [16], [25], [26], [29], [30], [33], [34], [36]. The fuzzy control algorithms attempt to make use of the information obtained from human experts, which is generally represented by fuzzy terms.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, based on the universal approximation theorem [29], [30], [32] (fuzzy logic systems have been shown to be capable of uniformly approximating any well-defined nonlinear function to any degree of accuracy), many important adaptive fuzzy control schemes have been developed to directly incorporate the expert information systematically and various stable performance criteria are guaranteed by theoretical analyses [8], [25], [26], [29], [30], [33]. In [30], two identifiers of nonlinear dynamic systems are developed based on the fuzzy system models.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed fuzzy identifiers are shown to be capable of following the output of a very general nonlinear dynamic system to an arbitrary accuracy in any finite time interval and all signals in the fuzzy identifiers are shown to be uniformly bounded. Yin and Lee in [33] have proposed a fuzzy model-reference adaptive control for controlling a plant with unknown parameters, dependent on the known variables. The fuzzy basis function expansion has been used to represent the unknown parameters and the convergence and stability properties of the fuzzy model-reference adaptive control are preserved.…”

Section: Introductionmentioning

confidence: 99%

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Robust tracking designs for both holonomic and nonholonomic constrained mechanical systems: adaptive fuzzy approach

Chang

Chen

2000

IEEE Trans. Fuzzy Syst.

106

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Adaptive fuzzy-based tracking control designs will be proposed in this paper for both holonomic mechanical systems as well as a large class of nonholonomic mechanical systems with plant uncertainties and external disturbances. A unified and systematic procedure is employed to derive the controllers for both holonomic and nonholonomic mechanical control systems, respectively. First, a fuzzy logic system is introduced to learn the behavior of unknown (or uncertain) mechanical dynamics by using an adaptive algorithm. Next, the effect of approximation error on the tracking error must be efficiently eliminated by employing an additional robustifying algorithm. Consequently, hybrid adaptive-robust controllers can be constructed such that the resulting closed-loop mechanical systems guarantee a satisfactorily transient and asymptotic performance. Furthermore, a partitioned procedure with respect to the above developed adaptive fuzzy logic approximators is introduced such that the number of fuzzy IF-THEN rules is significantly reduced and the developed control schemes can be easily implemented from the viewpoint of practical applications. Finally, simulation examples are presented to illustrate the tracking performance of a two-link constrained robot manipulator and a vertical wheel rolling on a plane surface by the proposed adaptive fuzzy-based control algorithms.Index Terms-Adaptive fuzzy approach, constrained mechanical system, partitioned procedure, robust tracking design.

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Section: B Controller Design and Stability Analysis For Holonomic Comentioning

confidence: 94%

Section: B Controller Design and Stability Analysis For Holonomic Comentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust tracking designs for both holonomic and nonholonomic constrained mechanical systems: adaptive fuzzy approach

Chang

Chen

2000

IEEE Trans. Fuzzy Syst.

106

View full text Add to dashboard Cite

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Synthetic inertia control based on fuzzy adaptive differential evolution

Chamorro

Riano

Gerndt

et al. 2019

International Journal of Electrical Power & Energy Systems

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The transformation of the traditional transmission power systems due to the current rise of non-synchronous generation on it presents new engineering challenges. One of the challenges is the degradation of the inertial response due to the large penetration of high power converters used for the interconnection of renewables energy sources. The addition of a supplementary synthetic inertia control loop can contribute to the improvement of the inertial response. This paper proposes the application of a novel Fuzzy Adaptive Differential Evolution (FADE) algorithm for the tuning of a fuzzy controller for the improvement of the synthetic inertia control in power systems. The method is validated with two test power systems: (i) an aggregated power system and its purpose is to understand the controller-system behavior, and (ii) a two-area test power system where one of the synchronous machine has been replaced by a a full aggregated model of a Wind Turbine Generator (WTG), whereby different limits in the tuning process can be analyzed. Results demonstrate the evolution of the membership functions and the inertial response enhancement in the respective test cases. Moreover, the appropriate tuning of the controller shows that it is possible to substantially reduce the instantaneous frequency deviation.

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