Adaptive fuzzy output feedback control of uncertain nonlinear systems with nonsymmetric dead-zone input

Zhou, N.; Liu, Yan Jun; Tong, Shaocheng

doi:10.1007/s11071-010-9836-x

Cited by 38 publications

(16 citation statements)

References 31 publications

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“…Now to show the stability of the error dynamics (21) and (24) with respect to the updating laws (19) and (20), let us introduce the following candidate Lyapunov-Krasovskii function…”

Section: Design Without Input Nonlinearitymentioning

confidence: 99%

“…Motivated by the removing these effects, for the nonchaotic systems, one can find several proposed techniques in literature (see [13][14][15][16][17][18]). Beside, some earlier works concerning the chaos system control with only input nonlinearities can be mentioned in [19][20][21][22][23][24]. Adaptive control methods integrated with approximation procedure of nonlinearities input function are effective to deal with the input nonlinearity effects in the chaotic systems is.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [19] and [20] have proposed approximation methods to tackle unknown dead-zone nonlinearity using fuzzy-neural network and fuzzy logic systems. Other approaches are also investigated in some authors such as the backstepping control method [21], fuzzy control systems [22], sliding mode control [23], and the adaptive variable-structure control method [24].…”

Section: Introductionmentioning

confidence: 99%

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Observer-based model reference adaptive control for unknown time-delay chaotic systems with input nonlinearity

2011

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Existence of unknown time-delay in the systems is a drastic restriction that it can menace the stability criteria and even deteriorate the performance system. This undesired case would be more intensified if that the uncertain input nonlinearity effects are also considered. To handle the input nonlinearities effects (results in dead-zone and/or hysteresis phenomena) and also unknown time-delay in the chaotic systems, this paper presents an observer-based Model Reference Adaptive Control (MRAC) scheme for a class of unknown time-delay chaotic systems with disturbances. This new method is a delay-independent variable-structure control method which is integrated with an observer system. The main task of the proposed approach is to accomplish a perfect tracking procedure such that unknown parameters are adapted via output estimation error. Furthermore, stability of the closed-loop system is achieved by means of the Lyapunov stability theory. Finally, the proposed methods are applied to some famous chaotic systems to verify the effectiveness of the proposed methods.

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“…Now to show the stability of the error dynamics (21) and (24) with respect to the updating laws (19) and (20), let us introduce the following candidate Lyapunov-Krasovskii function…”

Section: Design Without Input Nonlinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Observer-based model reference adaptive control for unknown time-delay chaotic systems with input nonlinearity

2011

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“…The prominent characteristic of a chaotic system is its extreme sensitivity to initial conditions and the system's parameters. Over the past decades, chaos control has been widely investigated and many researches have been studied in this field [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In [1], a linear feedback control method is proposed for controlling uncertain L€ u system.…”

Section: Introductionmentioning

confidence: 99%

Chaos control of uncertain time‐delay chaotic systems with input dead‐zone nonlinearity

Pai

2014

Complexity

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This article presents an adaptive sliding mode control (SMC) scheme for the stabilization problem of uncertain time-delay chaotic systems with input dead-zone nonlinearity. The algorithm is based on SMC, adaptive control, and linear matrix inequality technique. Using Lyapunov stability theorem, the proposed control scheme guarantees the stability of overall closed-loop uncertain time-delay chaotic system with input dead-zone nonlinearity. It is shown that the state trajectories converge to zero asymptotically in the presence of input dead-zone nonlinearity, time-delays, nonlinear real-valued functions, parameter uncertainties, and external disturbances simultaneously. The selection of sliding surface and the design of control law are two important issues, which have been addressed. Moreover, the knowledge of upper bound of uncertainties is not required. The reaching phase and chattering phenomenon are eliminated. Simulation results demonstrate the effectiveness and robustness of the proposed scheme.

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“…For systems with high uncertainty, which cannot be modeled or repeatable, adaptive neural control approach has obtained further development with the help of neural network (NN) approximation (e.g., [1][2][3][4][5]). Especially, for a wide class of non-matching uncertain nonlinear systems, many prospective adaptive control methodologies were proposed based on the idea of backstepping design approach by fusion of fuzzy approximation (e.g., [6][7][8][9][10][11][12][13][14]), and NN approximation (e.g., [15][16][17][18][19][20][21][22][23][24][25][26]). However, these control methods suffer from either a problem of computational explosion or a problem of dimension curse, or both problems.…”

Section: Introductionmentioning

confidence: 99%

Decentralized adaptive neural control of nonlinear systems with unknown time delays

2011

Nonlinear Dyn

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In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of "computational explosion," "dimension curse" and "controller singularity" are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.

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Adaptive fuzzy output feedback control of uncertain nonlinear systems with nonsymmetric dead-zone input

Cited by 38 publications

References 31 publications

Observer-based model reference adaptive control for unknown time-delay chaotic systems with input nonlinearity

Observer-based model reference adaptive control for unknown time-delay chaotic systems with input nonlinearity

Chaos control of uncertain time‐delay chaotic systems with input dead‐zone nonlinearity

Decentralized adaptive neural control of nonlinear systems with unknown time delays

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