Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems

Chen, Bing; Liu, Xiaoping; Tong, Shaocheng

doi:10.1109/tfuzz.2006.880008

Cited by 255 publications

(116 citation statements)

References 24 publications

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“…The backstepping technique, which is one of the main techniques used to design lower triangular systems, was originally proposed in a previous work [17]. Many so-called approximation-based adaptive backstepping control methods, which combine the universal function approximators with the adaptive backstepping technique, have been found to be particularly useful for nonlinear systems with triangular structures, and many representative works have been developed [1][2][3][4][5][6][18][19][20][21][22][23][24]28,[33][34][35][36]44,45].…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Neural Control of Uncertain Pure-Feedback Systems with Tracking Accuracy Known a Priori via Backstepping Design

Liu

et al. 2015

Circuits Syst Signal Process

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This paper concerns the adaptive neural network tracking control problem for a class of uncertain pure-feedback nonlinear systems. The main features of the proposed control scheme are shown here and compared to those of existing uncertain pure-feedback systems. First, the accuracy of the ultimate tracking error is given a priori. However, the tracking accuracy of the system cannot be determined a priori if existing control methods are used. Second, unlike in the case of traditional adaptive neural network control systems, the convergence of the tracking error was here analyzed using Barbalat's lemma based on nonnegative functions rather than Lyapunov functions. Third, with help from two novel nth-order continuously differentiable switching functions, the ultimate tracking error can be proven to reach accuracy given a priori and all closed-loop signals are uniformly ultimately bounded. Finally, a simulation is provided to illustrate the effectiveness of the proposed control approach.

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Section: Introductionmentioning

confidence: 99%

Direct Adaptive Neural Control of Uncertain Pure-Feedback Systems with Tracking Accuracy Known a Priori via Backstepping Design

Liu

et al. 2015

Circuits Syst Signal Process

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“…Due to the theoretical development as well as practical applications, analysis and synthesis of switched systems have recently gained considerable attention [2][3][4]. Since time delay frequently appears in the real systems and is a source of the poor performance and even instability, switched delay system has been extensively investigated [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Asynchronous H∞ filtering for switched stochastic systems with time-varying delay

Lian

Shi

2013

Information Sciences

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Abstract-This paper considers the H ∞ filtering problem of discrete-time switched delay systems. Attention is focused on the design of an exponentially mean-square stable filter taking the asynchronous switching and missing measurements into account. New results on exponential mean-square stability and a weighted l 2 -gain analysis for filtering error system are given, where the system is allowed to be unstable during the unmatched interval, in which the switching signal of filter is different from that of the system. By using the average dwell time (ADT) method and the Lyapunov-Krasovskii function method, delay-dependent sufficient conditions for the desired H ∞ filter are derived in terms of linear matrix inequalities (LMIs). A numerical example is provided to demonstrate the effectiveness of the proposed design approach.

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“…Multilayer neural networks, recurrent neural networks and dynamic neural network [10][11][12][13][14][15][16] were used for the design of adaptive controllers. On the other hand, fuzzy logic system, fuzzy neural network, recurrent fuzzy neural networks and dynamic fuzzy neural network were also a popular tool for the design of adaptive controllers [17][18][19][20][21][22]. These concepts have also been applied to the design of adaptive iterative learning control of nonlinear plants [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

Wang

Chien

2012

Journal of Control Science and Engineering

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We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error.

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Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems

Cited by 255 publications

References 24 publications

Direct Adaptive Neural Control of Uncertain Pure-Feedback Systems with Tracking Accuracy Known a Priori via Backstepping Design

Direct Adaptive Neural Control of Uncertain Pure-Feedback Systems with Tracking Accuracy Known a Priori via Backstepping Design

Asynchronous H∞ filtering for switched stochastic systems with time-varying delay

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

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