Observer-based adaptive fuzzy controller for nonlinear systems with unknown control directions and input saturation

Askari, Mohammad Reza; Shahrokhi, Mohammad; Talkhoncheh, Mahdi Khajeh

doi:10.1016/j.fss.2016.05.004

Cited by 49 publications

(20 citation statements)

References 38 publications

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“…Substituting (20), (22), (30), (31), and (33) into (29) and knowing that i1 S i n i (z i1 ) ≤ 0, one getṡ…”

Section: Adaptive Controller Design and Stability Analysismentioning

confidence: 99%

“…Consider the uncertain MIMO arbitrarily switched nonlinear system (1) under Assumptions 1-3 subject to input nonlinearity (2) and unmodeled dynamics. Applying control laws (25), virtual controllers (22), (23), and adaptation laws (24) guarantee the SGUUB of all the closed-loop signals and the tracking errors converge to their corresponding predefined regions.…”

Section: Adaptive Controller Design and Stability Analysismentioning

confidence: 99%

“…The system dynamics may be totally unknown or it can be in the form of parametric uncertainty. In the former case, an intelligent approximator like fuzzy logic systems (FLSs) or neural networks (NNs) can be used to estimate the system unknown dynamics, whereas, for the latter case, some adaptive laws for updating the estimates of unknown parameters can be employed . Likewise, the design of adaptive controller for switched systems with unknown dynamics has been addressed in the works of Li and Tong and Jiang et al, and several adaptive controllers for a class of switched systems with parametric uncertainties have been proposed in related works .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive prescribed performance control of switched MIMO uncertain nonlinear systems subject to unmodeled dynamics and input nonlinearities

Malek

Shahrokhi

Vafa

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, the design of an adaptive tracking control for a class of switched uncertain multiple‐input–multiple‐output nonlinear systems in the strict‐feedback form with unmodeled dynamics in the presence of three types of input nonlinearity under arbitrary switching has been addressed. By means of an intelligent approximator like a fuzzy logic system or a neural network, the unknown dynamics are estimated. The unmodeled dynamics have been tackled with a dynamic signal. A universal framework for describing different types of input nonlinearity including saturation, backlash, and dead zone has been utilized. By applying the backstepping approach and the common Lyapunov function method, virtual and actual controllers and the adaptive law for each subsystem have been constructed. Finally, it has been shown that the closed‐loop system is semiglobally uniformly ultimately bounded and the tracking errors converge to their predefined bounds. The effectiveness of the proposed control scheme has been shown through simulation study.

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“…Substituting (20), (22), (30), (31), and (33) into (29) and knowing that i1 S i n i (z i1 ) ≤ 0, one getṡ…”

Section: Adaptive Controller Design and Stability Analysismentioning

confidence: 99%

Section: Adaptive Controller Design and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive prescribed performance control of switched MIMO uncertain nonlinear systems subject to unmodeled dynamics and input nonlinearities

Malek

Shahrokhi

Vafa

et al. 2018

Intl J Robust & Nonlinear

Self Cite

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“…The constrained backstepping controller for input‐saturated systems was studied in several works . Furthermore, adaptive fuzzy control is employed considering unknown control directions for input saturated nonlinear systems with unmeasured states . In, adaptive fuzzy control was presented for pure‐feedback stochastic nonlinear systems by input saturation.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

Homayoun

Arefi

Vafamand

2020

Intl J Robust & Nonlinear

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This paper studies the problem of adaptive observer-based radial basis function neural network tracking control for a class of strict-feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non-smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so-called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system.Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design. K E Y W O R D Sadaptive neural network, command filter, input saturation, minimal learning parameter, observer, stochastic nonlinear systems 1 3296

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“…Also, input saturation is a common phenomenon in the practical project. The hyperbolic tangent function and Nussbaum function are employed to handle the saturation in Chen et al 21 and Askari et al 22 In Chen and Yu 23 and Li et al, 24 the auxiliary systems are constructed to compensate the influence of input saturation on tracking error. In Rehan et al, 25 the antiwindup compensator (AWC) is designed for Lipschitz nonlinear systems with input saturation, and its necessary condition is the stabilizing controller.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping control for a class of constrained non-affine nonlinear systems via self-organizing Hermite-polynomial-based neural network disturbance observer

Zhang

Wang

2017

Advances in Mechanical Engineering

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The article proposes a robust control approach based on self-organizing Hermite-polynomial-based neural network disturbance observer for a class of non-affine nonlinear systems with input saturation, state constraint, and unknown compound disturbance. Using Taylor series expansion, a hyperbolic tangent function, the non-affine nonlinear system with input saturation is transformed into time-varying affine system without input saturation, which can reduce step n + 1 of the backstepping technique compared with conventional method. Next, a self-organizing Hermite-polynomial-based neural network disturbance observer is proposed to estimate the compound disturbance online. Then, the auxiliary systems are designed to solve state constraint for subsystems, and hyperbolic tangent function is used to approximate the saturated control input. Simulation results proved the effectiveness of the proposed control scheme.

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Observer-based adaptive fuzzy controller for nonlinear systems with unknown control directions and input saturation

Cited by 49 publications

References 38 publications

Adaptive prescribed performance control of switched MIMO uncertain nonlinear systems subject to unmodeled dynamics and input nonlinearities

Adaptive prescribed performance control of switched MIMO uncertain nonlinear systems subject to unmodeled dynamics and input nonlinearities

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

Robust adaptive backstepping control for a class of constrained non-affine nonlinear systems via self-organizing Hermite-polynomial-based neural network disturbance observer

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