Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach

Li, Hongyi; Wu, Chengwei; Shi, Peng; Gao, Yabin

doi:10.1109/tcyb.2014.2371814

Cited by 310 publications

(107 citation statements)

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“…Those include time-delays, packet dropouts or quantization [29], [30]. It would be interesting to explore the possibilities of augmenting the presented theory in such directions.…”

Section: Discussionmentioning

confidence: 99%

Local Lyapunov Functions for Consensus in Switching Nonlinear Systems

Thunberg

2017

IEEE Trans. Automat. Contr.

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Abstract-This note presents two theorems on asymptotic state consensus of continuous time nonlinear multi-agent systems. The agents reside in R m and have switching interconnection topologies. Both the first theorem, formulated in terms of the states of individual agents, and the second theorem, formulated in terms of the pairwise states for pairs of agents, can be interpreted as variants of Lyapunov's second method. The two theorems complement each other; the second provides stronger convergence results under weaker graph topology assumptions, whereas the first often can be applied in a wider context in terms of the structure of the right-hand sides of the systems. The second theorem also sheds some new light on well-known results for consensus of nonlinear systems where the right-hand sides of the agents' dynamics are convex combinations of directions to neighboring agents. For such systems, instead of proving consensus by using the theory of contracting convex sets, a local quadratic Lyapunov function can be used.

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“…Those include time-delays, packet dropouts or quantization [29], [30]. It would be interesting to explore the possibilities of augmenting the presented theory in such directions.…”

Section: Discussionmentioning

confidence: 99%

Local Lyapunov Functions for Consensus in Switching Nonlinear Systems

Thunberg

2017

IEEE Trans. Automat. Contr.

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“…And the algorithm of the control system can't ensure the global optimal value of weight [3]. The fuzzy logic system can also approximate any nonlinear function to any prescribed accuracy, and it has the advantages in dealing with the time-delay, time-varying, multi input single output nonlinear system [4]. Especially, the combination between fuzzy control and PID control algorithm, the objective of improved control algorithm is to eliminate the steady state error, and the algorithm can also improve the control accuracy and stationary performance.…”

Section: Introductionmentioning

confidence: 99%

Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

Kong

Yao

2018

Int. J. Onl. Eng.

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Abstract-The control of nonlinear system is the hotspot in the control field. The paper proposes an algorithm to solve the tracking and robustness problem for the discrete-time nonlinear system. The completed control algorithm contains three parts. First, the dynamic linearization model of nonlinear system is designed based on Model Free Adaptive Control, whose model parameters are calculated by the input and output data of system. Second, the model error is estimated using the Quasi-sliding mode control algorithm, hence, the whole model of system is estimated. Finally, the neural network PID controller is designed to get the optimal control law. The convergence and BIBO stability of the control system is proved by the Lyapunov function. The simulation results in the linear and nonlinear system validate the effectiveness and robustness of the algorithm. The robustness effort of Quasi-sliding mode control algorithm in nonlinear system is also verified in the paper.Keywords-dynamic linearization model, quasi-sliding mode control, nonlinear system, neural network IntroductionWith increasing demands on the improvement of the precision control, the control problem of nonlinear system is attracting more and more attention [1]. Especially, the robustness problem of nonlinear control system model has gradually become an important topic in control theory. In general, control algorithms of nonlinear system mainly contain the neural network control, fuzzy logic control, the sliding mode control and model free adaptive control (MFAC).From universal approximation theory, a single hidden layer neural network can approximate any nonlinear function to any prescribed accuracy if sufficient hidden neurons are provided [2]. However, the training examples are usually much larger than the hidden nodes, and selecting the appropriate number of hidden nodes is the key factor which determining the error of the prediction model. And the algorithm of the control system can't ensure the global optimal value of weight [3]. The fuzzy logic system can also approximate any nonlinear function to any prescribed accuracy, and it has the advantages in dealing with the time-delay, time-varying, multi input single output nonlinear system [4]. Especially, the combination between fuzzy control and PID iJOE

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“…Since then, it has attracted great attention and many fruitful results have been presented in both theory and practice (see, e.g. [9][10][11][12][13][14][15]). One motivation for studying such a class of systems is that type-2 fuzzy sets are better in representing and capturing uncertainties [16,17], especially when the nonlinear plant inevitably suffers the parameter uncertainties while type-1 fuzzy sets do not contain uncertain information.…”

Section: Introductionmentioning

confidence: 99%

Control design for interval type‐2 polynomial fuzzy‐model‐based systems with time‐varying delay

Li¹,

Lam²,

Zhang

2017

IET Control Theory & Applications

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Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Abstract: In this paper, the problems of stabilization for interval type-2 polynomial fuzzy systems with time-varying delay and parameter uncertainties are investigated. The objective is to design a state-feedback interval type-2 polynomial fuzzy controller such that the closed-loop control system is asymptotically stable. The conditions for the existence of such a controller are delay dependent and membership function dependent in terms of sum-of-squares (SOS). Based on a basic lemma to deal with the delay terms, we formulate and solve the problem with more flexibility due to imperfect premise matching that the number of rules and premise membership functions are not necessary the same between the interval type-2 polynomial fuzzy model and interval type-2 polynomial fuzzy controller. Piecewise linear membership functions approximations enclosing the original lower and upper membership functions are employed to facilitate the stability analysis. A numerical example indicates the effectiveness of the derived results.

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Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach

Cited by 310 publications

References 38 publications

Local Lyapunov Functions for Consensus in Switching Nonlinear Systems

Local Lyapunov Functions for Consensus in Switching Nonlinear Systems

Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

Control design for interval type‐2 polynomial fuzzy‐model‐based systems with time‐varying delay

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