Output-feedback adaptive fuzzy control for a class of non-linear time-varying delay systems with unknown control directions

Yue, Haosong; Li, Jie

doi:10.1049/iet-cta.2011.0226

Cited by 41 publications

(61 citation statements)

References 30 publications

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“…This fact implies that they are best described by nonlinear models rather than by linear ones [6,9,63]. Therefore, a general setting for the control problem is to consider a set of nonlinear equations describing the system .…”

Section: Introductionmentioning

confidence: 99%

“…Various robust adaptive fuzzy sliding mode controllers have been presented for a class of nonlinear fractional-order systems with unknown control direction [6,9,38,52]. In [2,10,11,[47][48][49][50][51][60][61][62][63], five methods have been used to cope with the unknown control direction problem: (1) a method based on a Nussbaum-type function, (2) a method based on directly estimating unknown parameters, (3) a method based on a monitoring function, (4) a method based on a hysteresis-type function, and (5) a method based on a hysteresis dead-zone-type function and a Nussbaum function. Compared with the existing controls in [2,8,10,11,40,62], the adaptive fuzzy control laws presented in [47][48][49] have solved the tracking problem for nonlinear uncertain discrete-time systems with unknown control direction and input nonlinearities (such as dead zone, backlash-like hysteresis, and backlash), by using the reinforcement learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

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This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input-multioutput nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup property of fractional-order derivative, the system is transformed into a normalized fractional-order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real condition, the Razumikhin Lemma, the frequency-distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof. The main advantages of this work are that:(1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

View full text Add to dashboard Cite

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“…On the other side, differential flatness theory stands for a major direction in the design of nonlinear control systems [21][22][23][24][25][26][27]. It is known that neurofuzzy approximators can be used in indirect adaptive control schemes where their role is to identify online the unknown system dynamics and to provide the control with this information that is used for generating the control inputs [28][29][30]. Adaptive fuzzy control based on differential flatness theory extends the class of systems to which indirect adaptive fuzzy control can be applied [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Rigatos

Siano

2016

Intell Ind Syst

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The paper proposes a solution to the problem of control of nonlinear chaotic dynamical systems, which is based on differential flatness theory and on adaptive fuzzy control. An adaptive fuzzy controller is designed for chaotic dynamical systems, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the chaotic system's model into a linear form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neurofuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the adaptive fuzzy control method is checked through simulation experiments, using as case study the Lorenz chaotic oscillator.

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“…Efforts toward understanding the output-feedback control problem of stochastic nonlinear systems were initiated in [4], which is the first result to extend the output-feedback problem to the stochastic setting. In [46], an observer-based adaptive backstepping control scheme of the output-feedback problem was presented to ensure a closed-loop system is global asymptotic stable in probability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

Liu

Niu

Chu

et al. 2015

Circuits Syst Signal Process

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In this paper, a design scheme for an adaptive fuzzy tracking controller is proposed for a class of switched stochastic nonlinear time-delay systems via dynamic output-feedback. First, a reduced-order observer is introduced to estimate the unmeasurable states of the switched system. During the adaptive controller design procedure, an appropriate stochastic Lyapunov-Krasovskii functional deals with the time-delay terms, and fuzzy logic systems are employed to approximate the unknown nonlinearities. Based on the designed controller, the semi-globally uniform ultimate boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small Circuits Syst Signal Process neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed approach.

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Output-feedback adaptive fuzzy control for a class of non-linear time-varying delay systems with unknown control directions

Cited by 41 publications

References 30 publications

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

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