Adaptive Consensus Control of High-Order Uncertain Nonlinear Multi-agent Systems with Fuzzy Dead-Zone

Shahriari-kahkeshi, Maryam; Afrush, A.; Pham, Viet–Thanh

doi:10.1007/s40815-020-01005-6

Cited by 11 publications

(15 citation statements)

References 34 publications

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“…As it is obtained from (),

\overline{o}

is bounded and satisfies

\mid \overline{o}\mid <\max \left\{\overline{k}\left|{\delta}_r\right|,\overline{k}\left|{\delta}_l\right|\right\}

.Remark Despite there exist several adaptive control methods for the considered nonlinear system () with fuzzy dead‐zone in References [8–13], but these approaches are developed in the sense of full state measurement assumption. To relax this assumption, output feedback‐based control schemes were proposed in References [3, 14–16, 24, 25].…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…But, in practice, dead‐zone output is uncertain and it would be better to describe it by a fuzzy value. Therefore, some works focus on controller design for uncertain nonlinear systems with fuzzy dead‐zone 8–13 . In References [8–13], it is assumed that all state variables are measurable however in most of the physical systems, measuring all state variables is highly cost and even impossible.…”

Section: Introductionmentioning

confidence: 99%

“…Main contributions of this paper compared to the existing results are summarized as follows. Unlike most of the previous works on control of uncertain nonlinear systems with fuzzy dead‐zone that assume all of the state variables are measurable, 8–13 this paper assumes that only output variable is measurable and proposes RBFNN high gain observer for estimating unmeasurable states. After that, RBFNN observer‐based output feedback control algorithm is designed.…”

Section: Introductionmentioning

confidence: 99%

“…After that, RBFNN observer‐based output feedback control algorithm is designed. Therefore, because of eliminating full state measurement assumption, the proposed scheme is more suitable for practical systems. Almost all of the existing works, for instance, References [13, 15, 16, 26–31, 42], use CDSC approach to avoid the “explosion of complexity” problem. The CDSC approach uses first‐order filter to obtain derivative of the virtual control inputs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Shahriari-kahkeshi

Jahangiri‐heidari

Shi

2023

Adaptive Control & Signal

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This study proposes a high gain observer-based filtered backstepping control scheme for nonlinear systems with unknown dynamics and dead-zone constraint. Majority of the previously papers describe dead-zone by the conventional certain models, assume that all state variables are available and invoke the backstepping or conventional dynamic surface control (CDSC) approaches for controller design. This work describes the dead-zone by the fuzzy model, invokes radial basis function neural network (RBFNN) to model the unknown functions and proposes a RBFNN-based high gain observer to estimate unmeasurable states. To avoid the complexity explosion in the backstepping approach and eliminate the sensitivity to the time-constant of the first-order filters in the CDSC, a nonlinear tracking differentiator (NTD) with finite time convergence property is used instead of the first-order filters in the CDSC to obtain derivative of the virtual inputs. Moreover, the error compensation mechanism and auxiliary signal are proposed to eliminate the influence of the filtering error and input nonlinearity, respectively. Despite the fuzzy dead-zone, the proposed scheme makes the closed-loop signals uniformly ultimately bounded. In comparison with the existing results, (i) some assumptions like certain model of dead-zone and full state measurement are removed, (ii) "explosion of complexity" and sensitivity to the time constant of the first-order filters are eliminated, (iii) effect of the filtering error and input nonlinearity are compensated, (iv) number of adjustable parameters and online computational burden are decreased effectively. Simulation results on the two well-known examples verify the effectiveness and applicability of the proposed new design technique.

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“…As it is obtained from (),

\overline{o}

is bounded and satisfies

\mid \overline{o}\mid <\max \left\{\overline{k}\left|{\delta}_r\right|,\overline{k}\left|{\delta}_l\right|\right\}

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Shahriari-kahkeshi

Jahangiri‐heidari

Shi

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…In [20], the unknown function in the system is described by the mathematical model of an online neural network. In addition to this pioneering result, high-order system control based on neural networks has been widely developed and applied [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fixed-Time Control of Strict-Feedback High-Order Nonlinear Systems

Zhang

et al. 2021

Entropy

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This paper examines the adaptive control of high-order nonlinear systems with strict-feedback form. An adaptive fixed-time control scheme is designed for nonlinear systems with unknown uncertainties. In the design process of a backstepping controller, the Lyapunov function, an effective controller, and adaptive law are constructed. Combined with the fixed-time Lyapunov stability criterion, it is proved that the proposed control scheme can ensure the stability of the error system in finite time, and the convergence time is independent of the initial condition. Finally, simulation results verify the effectiveness of the proposed control strategy.

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Observer-Based Adaptive Fuzzy Distributed Control of Non-affine Multi-agent Systems with Input Quantization

Zhang

2022

Int. J. Fuzzy Syst.

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Adaptive Consensus Control of High-Order Uncertain Nonlinear Multi-agent Systems with Fuzzy Dead-Zone

Cited by 11 publications

References 34 publications

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Adaptive Fixed-Time Control of Strict-Feedback High-Order Nonlinear Systems

Observer-Based Adaptive Fuzzy Distributed Control of Non-affine Multi-agent Systems with Input Quantization

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