Hybrid-based adaptive NN backstepping control of strict-feedback systems

Huang, Jeng‐Tze

doi:10.1016/j.automatica.2009.02.012

Cited by 32 publications

(22 citation statements)

References 9 publications

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“…Remark 2. Assumptions 2 and 3 are some similar assumptions in the study of non-switched large-scale nonlinear systems in [4], [27], [29] By substituting (7), (8) and (9) into (3), the system given in (3) can be presented in the following forṁ…”

Section: B Fuzzy Logic Systems Approximation and System Transformationmentioning

confidence: 99%

“…Recently, much progress has been made in the field of decentralized control [27], [34], [40]. Particularly, approximation-based adaptive decentralized control approaches have been developed to uncertain largescale systems on the basis of neural networks (NNs) [24], [29] or fuzzy logic systems (FLSs) approximators [27], [37], [39], which are based on the reason that NNs or FLSs are universal approximators [5]- [8], [14] and have the ability to approximate nonlinear functions [31], [32]. However, most work focus on the decentralized control problem of nonswitched large-scale nonlinear systems and less attention has been paid to the decentralized control problem of switched large-scale nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems

Long

Zhao

2015

IEEE Trans. Fuzzy Syst.

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In this paper, a decentralized adaptive fuzzy outputfeedback control problem is discussed for a class of switched large-scale nonlinear systems without the measurements of the system states. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a switched observer is designed to avoid the conservativeness caused by adoption of a common observer for all subsystems. A common coordinate transformation of all subsystems is constructed to get rid of individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Meanwhile, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied in this paper, the classical average dwell time method cannot be directly applied. Based on this, a switched observer-based decentralized adaptive fuzzy output-feedback control technique is set up by exploiting the average dwell time method and backstepping. It is proved that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals with average dwell time, while the tracking errors converge to a small neighborhood of the origin. The effectiveness of the proposed control scheme is illustrated by its applications to a two inverted pendulums system and a quadruple-tank process system.

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Section: B Fuzzy Logic Systems Approximation and System Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems

Long

Zhao

2015

IEEE Trans. Fuzzy Syst.

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“…Recently, a great deal of progress has been achieved for controlling strictfeedback systems with unknown nonlinearities using the neural universal approximation [9]- [22]. However, the existence of unknown affine terms may cause the controller singularity problem and make the control design difficult [15]- [17]. To solve such a problem, integral Lyapunov functions have been widely applied in [18] and [19].…”

Section: Introductionmentioning

confidence: 99%

Learning From ISS-Modular Adaptive NN Control of Nonlinear Strict-Feedback Systems

Wang

Liu

et al. 2012

IEEE Trans. Neural Netw. Learning Syst.

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This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

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“…The benefit of using switching control is that it can achieve better stability results and transient performances that a single controller cannot [32], [33]. However, neither logic-based, hysteresis, nor continuous switching controls, such as those in [20], [34], and [35], are suitable for the dealing issue due to their nondifferentiable nature. Even the smooth switching is also inadequate owing to its disability of complete shutdown of the nominal controller outside the neural active region.…”

mentioning

confidence: 99%

“…Next the hybrid controller proposed in [20] is further incorporated to solve the control singularity problem. Meanwhile, it is found that the estimated error bounds have to be kept nonnegative for all time.…”

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confidence: 99%

Global Tracking Control of Strict-Feedback Systems Using Neural Networks

Huang¹

2012

IEEE Trans. Neural Netw. Learning Syst.

121

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Most existing adaptive neural controllers ensure semiglobally uniform ultimately bounded stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. As a result, deterioration of tracking performance or even instability may occur in real applications. A common recourse is to activate an extra robust controller outside the neural active region to pull back the transient. Such an approach, however, has been restricted to dynamic systems with matched uncertainty. We extend it to strict-feedback systems with mismatched uncertainties via multiswitching-based backstepping methodology. Each virtual and actual controller of the proposed design switches between an adaptive neural controller and a robust controller, with the switching algorithm being sufficiently smooth and, hence, able to be incorporated with the backstepping tool. The overall controller ensures globally uniform ultimate boundedness while simultaneously avoiding the possible control singularity. Simulation results demonstrate the validity of the proposed designs.

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Hybrid-based adaptive NN backstepping control of strict-feedback systems

Cited by 32 publications

References 9 publications

Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems

Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems

Learning From ISS-Modular Adaptive NN Control of Nonlinear Strict-Feedback Systems

Global Tracking Control of Strict-Feedback Systems Using Neural Networks

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