Two-Mode Adaptive Fuzzy Control With Approximation Error Estimator

Phan, Phi Anh; Gale, Timothy J.

doi:10.1109/tfuzz.2006.890657

Cited by 52 publications

(24 citation statements)

References 24 publications

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“…Theorem Consider the large-scale system (2) where the decentralized CAFC u i is given by (6)-(8), (3), and the parameters θ if , θ ig , θ iu , η i are adjusted by the adaptive laws (17)- (21). Let Assumptions 1-4 be true; then all the signals of the closed-loop large-scale system is guaranteed to stay UUB, and the tracking errors converge to tunable neighborhoods of the origin.…”

Section: Overall Algorithm and Stability Analysismentioning

confidence: 99%

“…Fuzzy controllers are able to allow for simple inclusion of heuristic knowledge or linguistic information about how to control plants instead of needing their exact mathematical models. Recently, a variety of indirect adaptive fuzzy or neural control algorithms have been developed for nonlinear systems by part of the control community [1][2][3][4][5][6]. In the meantime, many scholars have proposed various direct adaptive fuzzy or neural control schemes [2,[7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

Huang

Zhou

2010

Nonlinear Dyn

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In the previous work of Huang et al., a coordinated decentralized hybrid adaptive output feedback fuzzy control scheme of large-scale nonlinear systems is obtained predicated upon this prerequisite assumption that the local controllers can share the a priori information about their individual reference models. In this note, we concentrate in the absence of the coordination assumption on developing a classical decentralized combined indirect and direct adaptive fuzzy controller for a class of uncertain large-scale nonlinear systems. The output feedback and adaptation mechanisms proposed for each subsystem hinges just upon its individual output, regardless of any other output reference. Neither the famous strictly positive real (SPR) condition nor a high-gain observer (HGO) is required to realize the overall output feedback algorithm. The tracking errors of the closed-loop largescale system are shown to converge to tunable neighborhoods of the origin. Simulation results on correlated inverted pendulums verify the validity of the decentralized controller modification.

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Section: Overall Algorithm and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

Huang

Zhou

2010

Nonlinear Dyn

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“…We have proposed to use an approximation error estimator in [22]. This approximation error estimator scheme can also be applied to this direct AFC.…”

Section: Proofmentioning

confidence: 99%

“…In direct-type schemes, only one neuro-fuzzy system is used as a controller to approximate an ideal control law [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In the indirect-type, two neurofuzzy systems are used to approximate the non-linear dynamics of the plant [17][18][19][20][21][22]. Thus, it can be said that direct AFC is structurally simpler than indirect AFC.…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Fuzzy Control with Less Restrictions on the Control Gain

Phan

Gale

2006

2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference On

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Abstract:In the adaptive fuzzy control field for affine nonlinear systems, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In general, these restrictions are difficult to check in practice where mathematical models of plant are not available. In this paper, using a simple extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. The Lyapunov synthesis approach is used to guarantee the stability and convergence of the closed loop system. Finally, examples of an inverted pendulum and a magnet levitation system demonstrate the theoretical results.

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“…The last two decades or so have witnessed the amount of results on adaptive fuzzy and neural network control for SISO linear systems or nonlinear systems by means of indirect [1][2][3][4][5][6][7][8][9] or direct [5,6,[10][11][12][13][14][15][16][17] adaptive configurations. The pioneering indirect and direct adaptive fuzzy control (IAFC, DAFC) methodologies for three classes of cascaded MIMO uncertain nonlinear systems are developed using stepping technique in [9] and [16,17], respectively.…”

Section: Introductionmentioning

confidence: 99%

Decentralized direct adaptive output feedback fuzzy H ∞ tracking design of large-scale nonaffine nonlinear systems

2009

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A novel H ∞ tracking-based decentralized direct adaptive output feedback fuzzy controller is developed for a class of interconnected nonaffine uncertain nonlinear systems in this paper. By virtue of the proper filtering of the observation error dynamics to assure its strictly positive realness, the observer-based decentralized direct adaptive fuzzy control (DAFC) scheme is presented for a class of large-scale nonaffine nonlinear systems by the combination of H ∞ tracking technique, implicit function theorem, a state observer and a fuzzy inference system. The output feedback and adaptation mechanisms for each subsystem depend upon local measurements not only to achieve asymptotical tracking of a reference trajectory but to guarantee arbitrary small attenuation level of the mismatched errors and external disturbances on the tracking error. Simulation results confirm the effectiveness of the proposed decentralized output feedback scheme.

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Two-Mode Adaptive Fuzzy Control With Approximation Error Estimator

Cited by 52 publications

References 24 publications

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

Direct Adaptive Fuzzy Control with Less Restrictions on the Control Gain

Decentralized direct adaptive output feedback fuzzy H ∞ tracking design of large-scale nonaffine nonlinear systems

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