Stabilization for a class of nonlinear networked control systems via polynomial fuzzy model approach

Li, Hongyi; Chen, Ziran; Yi, Sun; Karimi, Hamid Reza

doi:10.1002/cplx.21579

Cited by 31 publications

(26 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we employ fuzzy logic systems in [29] to approximate the unknown nonlinear functions. The fuzzy rule base is made up of the following inference rules: R l : If x 1 is F l 1 and x 2 is F l 2 and .…”

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Chang

Tong

2016

Neural Comput & Applic

View full text Add to dashboard Cite

This paper deals with the problem of adaptive fuzzy tracking control for a single-link robotic manipulator coupled to a brushed direct current motor with actuator saturation. Fuzzy logic systems are used to approximate the unknown nonlinear systems. The filtered signals are introduced to eliminate the interference of high-frequency signals. The virtual/actual control inputs are derived from the solutions of a series of dynamical equations. Under the framework of the backstepping control design, an observerbased output feedback control design scheme is proposed. It is proved that the control approach can guarantee that all the signals in the closed-loop system are bounded.

show abstract

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Chang

Tong

2016

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Using the above mentioned multipliers, conservatism in local stability results can be reduced [93,97]. Polynomial and delay or sample-data techniques can be, too, combined [98]. In fact, many TS results with an LMI involving constant vertices A i and Lyapunov matrix P can be translated to polynomial-fuzzy with A i (x) and P (x) via straightforward developments (note thatx cannot be the full state x in some cases, see below): the standard V (x) = x T P x is translated to V = ζ(x) T P (x)ζ(x).…”

Section: Fuzzy-polynomial Techniquesmentioning

confidence: 99%

Fuzzy control turns 50: 10 years later

Guerra

Sala

Tanaka

2015

Fuzzy Sets and Systems

View full text Add to dashboard Cite

In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.

show abstract

“…Therefore, nonfragile control should be adopted for NCSs to tolerate the controller gain fluctuation. Accordingly, a lot of efforts have been devoted to these challenging issues in NCSs [5][6][7][8][9][10][11]. Very recently, the randomly occurring controller gain fluctuation has received increasing research attention, which is more complex but common in the applications [12,13].…”

Section: Introductionmentioning

confidence: 99%

Nonfragile passivity and passification of nonlinear singular networked control systems with randomly occurring controller gain fluctuation

Liu

2015

Complexity

View full text Add to dashboard Cite

In this article, the nonfragile passivity and passification problems are investigated for a class of nonlinear singular networked control systems (NCSs) with network-induced time-varying delay. In particular, the randomly occurring controller gain fluctuation is taken into consideration by introducing the stochastic variable satisfying the Bernoulli random distribution. By constructing proper Lyapunov-Krasovskii function, delay-dependent sufficient conditions are established to guarantee the passivity of the singular NCSs. Based on the derived results, the nonfragile passification controller is further designed in terms of linear matrix inequalities. Finally, a numerical example is provided to illustrate the applicability and effectiveness of our theoretical results.

show abstract

Stabilization for a class of nonlinear networked control systems via polynomial fuzzy model approach

Cited by 31 publications

References 44 publications

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Fuzzy control turns 50: 10 years later

Nonfragile passivity and passification of nonlinear singular networked control systems with randomly occurring controller gain fluctuation

Contact Info

Product

Resources

About