Time-Varying Delay Compensation for a Class of Nonlinear Control Systems Over Network via $H_{\infty }$ Adaptive Fuzzy Controller

Hamdy, Mohamed; Abd‐Elhaleem, Sameh; Fkirin, M. A.

doi:10.1109/tsmc.2016.2614779

Cited by 121 publications

(53 citation statements)

References 51 publications

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“…One of the future research directions should focus on the adaptive ETC of nonlinear systems with network induced time‐varying delays and random packet losses. () The extension of the proposed approach to handle the ETC problem of more general nonlinear systems() also deserves significant research attention.…”

Section: Discussionmentioning

confidence: 99%

Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems

Yang

2017

Intl J Robust & Nonlinear

119

115

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Summary This paper proposes a novel adaptive backstepping control method for parametric strict‐feedback nonlinear systems with event‐sampled state and input vectors via impulsive dynamical systems tools. In the design procedure, both the parameter estimator and the controller are aperiodically updated only at the event‐sampled instants. An adaptive event sampling condition is designed to determine the event sampling instants. A positive lower bound on the minimal intersample time is provided to avoid Zeno behavior. The closed‐loop stability of the adaptive event‐triggered control system is rigorously proved via Lyapunov analysis for both the continuous and jump dynamics. Compared with the periodic updates in the traditional adaptive backstepping design, the proposed method can reduce the computation and the transmission cost. The effectiveness of the proposed method is illustrated using 2 simulation examples.

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Section: Discussionmentioning

confidence: 99%

Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems

Yang

2017

Intl J Robust & Nonlinear

119

115

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“…The detailed steps to obtain (12) can be referred to the work of Pan et al 30 The detailed steps to obtain (12) can be referred to the work of Pan et al 30 …”

Section: Neural Network-based Backstepping Controlmentioning

confidence: 99%

“…whereW i ∶= W * i −Ŵ i is a parameter estimation error. The detailed steps to obtain (12) can be referred to the work of Pan et al 30…”

Section: Neural Network-based Backstepping Controlmentioning

confidence: 99%

“…for the ith subsystem of (12). Differentiating V i in (20) along (12) with respect to time t and using (8), one gets…”

Section: Proof First Noting Lemma 3 One Gets X(t)mentioning

confidence: 99%

“…1 Compared with the traditional adaptive control, the most appealing merit of NNAC is that the modelling difficulty in many practical control problems can be greatly mitigated resulting in the simplification of control synthesis for a wider class of nonlinear systems with functional uncertainties. 2 However, in most existing NNAC methods, eg, see some recent works, [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] the ability of NNs to learn plant uncertainties is not completely exploited and only tracking error convergence is available. The ability to learn for NNs, reflected by the convergence of NN weights, is guaranteed by the well-known condition termed persistent excitation (PE).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Composite learning adaptive backstepping control using neural networks with compact supports

Yang

Pratama

2019

Adaptive Control & Signal

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The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed-loop system is established under a weak excitation condition termed interval excitation.The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised-cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.

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Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Yousef

Hamdy

Saleem

et al. 2019

Adaptive Control & Signal

Self Cite

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This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced  adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab-scale PEA system.Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root-mean-square and maximum tracking errors for different frequencies.

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Time-Varying Delay Compensation for a Class of Nonlinear Control Systems Over Network via $H_{\infty }$ Adaptive Fuzzy Controller

Cited by 121 publications

References 51 publications

Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems

Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems

Composite learning adaptive backstepping control using neural networks with compact supports

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

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