Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Liu, Yan Jun; Zhou, N.

doi:10.1016/j.isatra.2010.06.002

Cited by 89 publications

(40 citation statements)

References 41 publications

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“…To illustrate the key features of the dead-zone in the control problems in this paper, we have the following assumptions [10,33]:…”

Section: Control Problem Statementmentioning

confidence: 99%

“…Then, the neural networks or fuzzy systems will be utilized to an approximate unknown function, and an adaptive control scheme is designed to estimate the unmeasured states [3,5,7,[10][11][12][13][22][23][24][25][26][27][28][29]32,37]. This means that we need not fix a priori the compact set over which the neural networks approximations are employed.…”

Section: Mnns Modelingmentioning

confidence: 99%

“…Then, the adaptive update laws which are both deduced by the switched Lyapunov function candidate (described later) can be expressed by [3,10,32,37]:…”

Section: Adaptive Controller Designmentioning

confidence: 99%

“…On the other hand, it is well known that dead-zone is one of the most important non-smooth nonlinearities in many industrial processes, such as electric circuits and electronic servomotors, and hydraulic and pneumatic valves [5,10,[26][27][28]33,37]. The presence of this input characteristic usually degrades the system performance.…”

Section: Introductionmentioning

confidence: 99%

“…The design of the adaptive neural controller is successfully used to overcome the design difficulties which are from the systems with unmeasured states, unknown nonlinear function and unknown dead-zone input [3,7,8,[11][12][13][22][23][24][25][26][27][28][29]32,37]. Thus, the study of dead-zone nonlinearities has been drawing much interest in the adaptive neural control community for a long time [10,14,15,20,21,26,28,37]. In [37], a novel adaptive neural control scheme for a class of uncertain MIMO nonlinear state time-varying delay systems with unknown nonlinear dead-zones and gain signs was presented.…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

Fei

Yang³

2015

Circuits Syst Signal Process

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This paper is concerned with adaptive neural tracking control problem for uncertain switched nonlinear systems with unknown dead-zone input. Multilayer neural networks (MNNs) are employed to approximate unknown nonlinear functions, and an adaptive neural network controller is introduced to enhance system robustness. With the proposed control scheme, boundedness of all the signals of the closed-loop system is established regardless of the parameter adjustment mechanism, and better tracking control performance can eventually be achieved in view of the universal approximation capability of MNNs. Also, a switching signal is suitably defined using average dwell-time technique. By using a switching control scheme, it is demonstrated that the transient performance and stability can be simultaneously obtained. Finally, a simulation example is given to illustrate the effectiveness and validity of this approach.

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“…To illustrate the key features of the dead-zone in the control problems in this paper, we have the following assumptions [10,33]:…”

Section: Control Problem Statementmentioning

confidence: 99%

Section: Mnns Modelingmentioning

confidence: 99%

“…Then, the adaptive update laws which are both deduced by the switched Lyapunov function candidate (described later) can be expressed by [3,10,32,37]:…”

Section: Adaptive Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

Fei

Yang³

2015

Circuits Syst Signal Process

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Adaptive prescribed performance control of nonlinear systems with unknown dead zone

2012

Adaptive Control & Signal

120

105

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SUMMARYAn alternative adaptive control with prescribed performance is proposed to address the output tracking of nonlinear systems with a nonlinear dead zone input. An appropriate function that characterizes the convergence rate, maximum overshoot, and steady‐state error is adopted and incorporated into an output error transformation, and thus the stabilization of the transformed system is sufficient to achieve original tracking control with prescribed performance. The nonlinear dead zone is represented as a time‐varying system and Nussbaum‐type functions are utilized to deal with the unknown control gain dynamics. A novel high‐order neural network with a scalar adaptive weight is developed to approximate unknown nonlinearities, thus the computational costs can be diminished dramatically. Some restrictive assumptions on the system dynamics and the dead‐zone are circumvented. Simulations are included to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

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Observer‐based adaptive control for stochastic nonstrict‐feedback systems with unknown backlash‐like hysteresis

Bai

Zhou

Wang

et al. 2017

Adaptive Control & Signal

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SummaryThis paper studies an observer‐based adaptive fuzzy control problem for stochastic nonlinear systems in nonstrict‐feedback form. The unknown backlash‐like hysteresis is considered in the systems. In the design process, the unknown nonlinearities and unavailable state variables are tackled by introducing the fuzzy logic systems and constructing a fuzzy observer, respectively. By using adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy control algorithm is developed. For the closed‐loop system, the proposed controller can guarantee all the signals are 4‐moment semiglobally uniformly ultimately bounded. Finally, simulation results further show the effectiveness of the presented control scheme.

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Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Cited by 89 publications

References 41 publications

A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

Adaptive prescribed performance control of nonlinear systems with unknown dead zone

Observer‐based adaptive control for stochastic nonstrict‐feedback systems with unknown backlash‐like hysteresis

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