Global Predefined Time and Accuracy Adaptive Neural Network Control for Uncertain Strict-Feedback Systems With Output Constraint and Dead Zone

Ni, Junkang; Shi, Peng

doi:10.1109/tsmc.2020.2994808

Cited by 89 publications

(58 citation statements)

References 44 publications

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“…Definition 2 [41]- [43] (Predefined-time stability): For the system (1), if it is fixed-time stability and exists a predefined constant T C such that the setting time function T (x 0 ) ≤ T C , the original point is named predefined-time stable, where constant T C is a function concerned with the system parameter ρ.…”

Section: B Predefined-time Stabilitymentioning

confidence: 99%

“…For example, [42] focused on Lyapunov-like conditions for a class of dynamical systems to achieve predefined-time stability. The authors in [43] presented a global predefinedtime control scheme for a class of nonlinear strict-feedback systems to achieve that the tracking error converge to a predefined accuracy and the authors in [44] proposed a predefinedtime control method for high-order integrator systems to achieve predefined-time convergence. In [45], the predefinedtime consensus tracking control problem was researched for second-order multiagent systems.…”

Section: Introductionmentioning

confidence: 99%

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Predefined-Time Adaptive Neural-Network-Based Consensus Tracking Control for Nonlinear Multiagent Systems With Zero Tracking Error

et al. 2021

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This paper is dedicated to addressing the predefined-time consensus tracking control problem for unknown high-order nonlinear multiagent systems. The prominent difference compared with some existing papers is that the follower is modeled in the form of non-strict-feedback structure. Meanwhile, instead of being constants, the real control gains are unknown functions. A distinct advantage in our work is that the outputs of followers are able to track the output of leader within the time specified in advance. In order to get our desired predefined-time controller, radial basis function (RBF) neural networks (NNS) are applied to compensate those unknown nonlinearities. Then in the design framework of adaptive backstepping, the predefined-time virtual control laws are presented and their derivatives are approximated by using finite-time differentiators. Under our proposed predefined-time controller, it is rigorously demonstrated that the whole closed-loop system remains stable and all outputs of the followers track the reference signal in predefined time. In the end, a simulation example is given to ulteriorly verify the efficacy of the suggested predefined-time control scheme.INDEX TERMS Predefined-time control, Adaptive tracking control, Nonlinear multiagent systems, Neural networks (NNs).

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Section: B Predefined-time Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predefined-Time Adaptive Neural-Network-Based Consensus Tracking Control for Nonlinear Multiagent Systems With Zero Tracking Error

et al. 2021

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“…, n, where h i and h i are unknown constants. Assumption 2 [35]: the trajectory signal y r is a continuous and bounded function. Usually, the time derivatives of y r is also continuous and bounded function.…”

Section: System Formulationmentioning

confidence: 99%

“…Recently, the advantages of predefined-time theory were applied to second-order multi-intelligent systems [33] and nonlinear mechanical systems [34]. In [35], according to the characteristic of predefined-time stability theory, an improved predefined-time adaptive control structure was proposed to deal with the system with dead zone. Furthermore, in [36], based on sliding mode control technology, a predefined-time controller was designed for second-order nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Differentiator‐Based Predefined‐Time Control for Nonlinear Systems Subject to Pure‐Feedback Form and Unknown Disturbance

Yang

Zhang

et al. 2021

Complexity

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In this article, by utilizing the predefined-time stability theory, the predefined-time output tracking control problem for perturbed uncertain nonlinear systems with pure-feedback structure is addressed. The nonaffine structure of the original system is simplified as an affine form via the property of the mean value theorem. Furthermore, the design difficulty from the uncertain nonlinear function is overcome by the excellent approximation performance of RBF neural networks (NNs). An adaptive predefined-time controller is designed by introducing the finite-time differentiator which is used to decrease the computational complexity problem appeared in the traditional backstepping control. It is proved that the proposed control method guarantees all signals in the closed-loop system remain bound and the tracking error converges to zero within the predefined time. Based on the controller designed in this paper, the expected results can be obtained in predefined time, which can be illustrated by the simulation results.

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“…Therefore, remaining the states within prescribed bounds has become a major research topic for the sake of safety consideration. BLF has been a powerful tool in guaranteeing full state constraints and some commonly seen forms include tan‐type BLF, 25 log‐type BLF, 26 and integral‐type BLF 27 . Generally, most of the existing results are concentrated on strict/pure feedback nonlinear systems with output constraints or full state constraints 28,29 .…”

Section: Introductionmentioning

confidence: 99%

Barrier Lypunov functions‐based nonsingular fixed‐time switching control for strict‐feedback nonlinear dynamics with full state constraints

Zhang

Dong

et al. 2021

Intl J Robust & Nonlinear

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This work proposes a nonsingular adaptive fixed‐time switching control method for a class of strict‐feedback nonlinear dynamics subject to full state constraints. The peculiarity of this design lies in overcoming the singularity issue that typically appears in the existing backstepping‐based fixed‐time control methods caused by the iterative differentiation of fractional power terms as tracking errors approach to zero, while guaranteeing the nonviolation of full state constraints. Crucial in solving such singularity issue is to skillfully introduce a smooth switching between fractional power and integer power terms, which guarantees that fractional power term is confined within a positive interval all the time. An asymmetric time‐varying barrier Lyapunov function is delicately incorporated into control design, rendering state variables to be within prescribed time‐varying bounds. Besides, radial basis function neural network is employed to handle system unknown nonlinearities. It is rigorously proved that all the closed‐loop signals eventually converge to small regions around origin within fixed‐time. Comparative simulation results are finally given to validate the effectiveness and superiority of the proposed control strategy.

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Global Predefined Time and Accuracy Adaptive Neural Network Control for Uncertain Strict-Feedback Systems With Output Constraint and Dead Zone

Cited by 89 publications

References 44 publications

Predefined-Time Adaptive Neural-Network-Based Consensus Tracking Control for Nonlinear Multiagent Systems With Zero Tracking Error

Predefined-Time Adaptive Neural-Network-Based Consensus Tracking Control for Nonlinear Multiagent Systems With Zero Tracking Error

Adaptive Differentiator‐Based Predefined‐Time Control for Nonlinear Systems Subject to Pure‐Feedback Form and Unknown Disturbance

Barrier Lypunov functions‐based nonsingular fixed‐time switching control for strict‐feedback nonlinear dynamics with full state constraints

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