Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors

Park, Jang-Hyun; Seo, Sam-Jun; Park, Gwi-Tae

doi:10.1016/s0165-0114(02)00137-9

Cited by 128 publications

(57 citation statements)

References 12 publications

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“…Moreover, two main below conditions must be satisfied in terms of multi-objective minimization problem in which the X1 solution determine as a dominant solution if both (16) and (17) are satisfied as below.…”

Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

“…In [10], fuzzy logic theory idea is used to investigate the optimal capacitor allocation problem. Recently, fuzzy set theory has caught the attraction of researcher for designing intelligent systems in broad range of applications such as electrical load forecasting [11], designing fuzzy controller for industrial robots [12,13], powerful tool for system uncertainty compensator and optimization method with high rate of accuracy and performance [14,15], adaptive model for non-linear controllers [16,17], etc. Here the system is modeled by using fuzzy membership function which most challenging issue would be the proper choose of membership functions and identify corresponding rule properly and precisely.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Wáng¹,

Cheong²,

Chén³

2017

IJCATR

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This paper presents a heuristics based approach for capacitor allocation problem incorporating Point Estimate Method (PEM) based analysis to attain optimal capacitor reallocation in radial distribution systems. The proposed probabilistic method is used to achieve optimal capacitor allocation associated with the active/reactive loads forecasted error as well as energy loss reduction by taking into account the cost function coefficients. This problem is formulated as a mix integer non-linear, discrete multi-objective problem which be in need of a robust optimization tool to avoid trapping in local optima. The proposed solution based on modified harmony search (MHS) algorithm employ to search for a set of Pareto optimal value of objective functions which save in memory that called repository. Moreover, a fuzzy clustering method is utilized to control the size memory (repository) for the concerned problem.The proposed approach has been tested on IEEE 9 buses to check feasibility and robustness of the proposed approach.

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Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Wáng¹,

Cheong²,

Chén³

2017

IJCATR

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“…Based on the universal approximation theorem, several stable adaptive fuzzy control schemes [9][10][11][12][13][14] have been developed to overcome the difficulty of extracting linguistic control rules from experts and to cope with the system parameter changes. An adaptive fuzzy system is a fuzzy logic system equipped with an adaptation algorithm to be able to update the fuzzy system parameters [15][16], and the fuzzy logic system is constructed from a collection of fuzzy IF-THEN rules.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Fuzzy Control of Uncertain Nonlinear Systems with Unknown Dead-Zone

Chiang¹

2013

AJIS

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This paper presents a robust adaptive fuzzy control scheme for a class of uncertain nonlinear systems containing an unknown dead-zone. Dead-zone characteristics are quite commonly encountered in actuators, such as hydraulic and pneumatic valves, electric servomotors, and electronic circuits, etc. Therefore, by using a description of a dead-zone and by exploring the properties of this dead-zone model intuitively and mathematically, a robust adaptive fuzzy control method is presented without constructing the dead-zone inverse. The unknown nonlinear functions of the controlled system are approximated by the fuzzy logic system according to some adaptive laws. By means of Lyapunov stability theorem, the proposed robust adaptive fuzzy control scheme can guarantee the robust stability of the whole closed-loop system with an unknown dead-zone in the actuator and obtain good tracking performance as well. Finally, an example and simulation results are illustrated to demonstrate the validity of the proposed method.

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“…However, it can be imagined that such complicated structuring learning may lead to computational load so that they are not suitable for online practical applications. Recently, some intelligent control schemes utilize the SFS approach proposed in Gao and Er (2003), Hsu (2007), Lin et al (2001) and Park et al (2003Park et al ( , 2005. However, some of them use the gradient descent method to derive the parameter learning algorithms which cannot guarantee the system stability (Lin et al 2001).…”

Section: Introductionmentioning

confidence: 99%

“…However, some of them use the gradient descent method to derive the parameter learning algorithms which cannot guarantee the system stability (Lin et al 2001). Some of them derive the parameter learning algorithms in the Lyapunov sense to guarantee system stability, but the structure learning algorithm is too complex (Gao and Er 2003;Hsu 2007;Park et al 2003Park et al , 2005. In Hsu (2007), Lin et al (2001) and Park et al (2005), a self-constructing fuzzy neural network control is proposed to avoid the newly generated membership function being too similar to the existing ones.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

et al. 2008

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In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.

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Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors

Cited by 128 publications

References 12 publications

Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Robust Adaptive Fuzzy Control of Uncertain Nonlinear Systems with Unknown Dead-Zone

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

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