Adaptive Neural Fixed-Time Tracking Control for High-Order Nonlinear Systems

Ma, Jiawei; Wang, Huanqing; Qiao, Junfei

doi:10.1109/tnnls.2022.3176625

Cited by 63 publications

(22 citation statements)

References 48 publications

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“…Finally, two illustrative studies confirmed the validity and practicability of the proposed theoretical result. In particular, inspired by some novel results, 55,56 we intend to extend the results of this study to adaptive fixed-time control for high-order/multiagent non-affine nonlinear systems in a future study.…”

Section: Discussionmentioning

confidence: 99%

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Song

Sun

Ahn

et al. 2023

Intl J Robust & Nonlinear

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This study developed a new strategy for adaptive fixed-time output feedback control of multi-input multi-output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock-in-place were simultaneously considered. Furthermore, a composite observer was used in MIMO systems to estimate unmeasurable states and external disturbances simultaneously, and radial basis function neural networks were employed to identify unknown internal nonlinearities. Additionally, the command-filtered backstepping approach was applied to avoid tedious analytic calculations of the backstepping framework, and a compensation item was constructed to attenuate the filter error. A fault-tolerant controller was developed to ensure that the overall states of the controlled system were practically fixed-time bounded while the tracking error was regulated to the equilibrium region having a fixed-time convergence ratio. Illustrative studies showed the validity and practicability of the proposed theory.

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Section: Discussionmentioning

confidence: 99%

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Song

Sun

Ahn

et al. 2023

Intl J Robust & Nonlinear

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“…As described in References 40‐45, if the elements in

f(Z)

belong to compact set

{\Omega}_Z

and nonlinear smooth functions

f(Z)

is continuous, then for any given constant

\varepsilon >0

, there are NNs

{W}^{\ast T}\Phi (Z)

such that

f(Z)={W}^{\ast T}\Phi (Z)+\delta (Z),\forall Z\in {\Omega}_Z,

where

{W}^{\ast }

is the ideal weight vector and it can be described as

{W}^{\ast }=\arg \underset{W\in {R}^l}{\min}\underset{Z\in {\Omega}_Z}{\sup}\mid f(Z)-{W}^{\ast T}\Phi (Z)\mid,

\delta (Z)

denotes the approximation error that satisfies the inequality

false...

…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

Jiang

Wang

2023

Adaptive Control & Signal

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Summary In this paper, a command‐filter‐based fast finite‐time composite adaptive control scheme is proposed for nonlinear systems with input dead‐zone. The neural networks (NNs) are used to approximate the unknown functions of the controlled systems. The series‐parallel nonsmooth estimation models are used to predict the errors. In addition, the tracking error and prediction errors are anastomosed to update the weights of the NNs. To solve the problem of “explosion of complexity,” the command‐filter technology is adopted. Combining the Lyapunov stability theory and the adaptive backstepping algorithm, a composite adaptive backstepping control scheme is proposed. The proposed scheme dissolves the singularity problem which may emerge in the design process and the neural networks approximation performances can be realized as well. Meanwhile, the proposed scheme guarantees the tracking error converges into a small neighborhood of zero in fast finite‐time and the boundedness of all the closed‐loop signals. Finally, the simulation example is provided to verify the effectiveness of the proposed scheme.

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“…In addition, an adaptive NN‐based backstepping output feedback optimal control was proposed for uncertain nonlinear systems with state constraints in Reference [22]. An adaptive neural fixed‐time tracking control was proposed for high‐order non‐strict nonlinear systems in Reference [23].…”

Section: Introductionmentioning

confidence: 99%

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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Adaptive Neural Fixed-Time Tracking Control for High-Order Nonlinear Systems

Cited by 63 publications

References 48 publications

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

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

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