Fixed-Time Adaptive Neural Network Control for Nonlinear Systems With Input Saturation

Sun, Wei; Diao, Shuzhen; Su, Shun‐Feng; Sun, Zong‐Yao

doi:10.1109/tnnls.2021.3105664

Cited by 56 publications

(18 citation statements)

References 47 publications

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“…Where m u is a bound on ( ) v t and v is the actual control law. and the following smooth function approximation is used to deal with the saturation function [13].…”

Section: Input Saturation Sectionmentioning

confidence: 99%

Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Gao,

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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This paper discusses an adaptive dynamic surface control method for uncertain strict feedback nonlinear systems with input saturation and external disturbance. The radial basis neural network (RBFNN) is used to approximate the unknown function and the hyperbolic tangent function is used to solve the input saturation problem. Meanwhile, the improved dynamic surface control technology reduces the influence of first-order filter on the state error, and the control scheme has excellent tracking performance and stability. Finally, the final bounded convergence of all closed-loop signals is proved, and the superiority of the proposed control scheme is verified by simulation.

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“…Where m u is a bound on ( ) v t and v is the actual control law. and the following smooth function approximation is used to deal with the saturation function [13].…”

Section: Input Saturation Sectionmentioning

confidence: 99%

Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Gao,

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…The stabilization of nonstrict feedback nonlinear systems has been reported by using feedback control, such as feedback control based on adaptive NNs [31,32], fuzzy systems [33-35, 4, 4]. Compared with [31-35, 4, 4], the difference is that intermittent control, as an effective discontinuous control, based on adaptive NNs is studied in this paper for the first time.…”

Section: Remarkmentioning

confidence: 99%

Finite time adaptive neural intermittent control for a class of nonlinear dynamical systems

Wang

Mashino

et al. 2023

Preprint

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In this paper, the semiglobal practical finite-time stable (SGPFTS) problem for a class of nonlinear nonstrict feedback systems is investigated by adaptive neural networks (NNs) intermittent control strategy. This work is motivated by that available differential inequalities to cope with intermittent control do not work for nonstrict feedback nonlinear system (NFNS), since the system dynamics is unknown. In order to deal with this problem, a novel differential inequality and adaptive neural intermittent control are established to handle intermittent control, which generalizes previous works. Then, based on this lemma with the proposed adaptive NNs intermittent control scheme, backstepping design process, finite-time technique and Lyapunov stability theory, sufficient conditions are presented to guarantee the achievement of SGPFTS for NFNS. Furthermore, a networked-based one-link arm dynamics system is applied to the theoretical results. Finally, corresponding simulation results are given to illustrate the effectiveness of the theoretical results and the practicability of the proposed control method.

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“…Hence, interest is growing in fixed-time stability with the settling time bounds that are independent of the initial conditions (Polyakov, 2012). Recently, the fixed-time controller design for different nonlinear systems has been studied in Polyakov et al (2015), Shi et al (2020), Sun et al (2021), and Mei et al (2022) and the references therein. Although the fixed-time control results can guarantee the system state converges to zero within the desired time by appropriately tuning the design parameters, it can only determine the upper bound of the convergence time and is still difficult to assign the convergence time arbitrarily.…”

Section: Introductionmentioning

confidence: 99%

Prescribed-time state-feedback stabilization of nonlinear systems with unmatched nonlinear terms

Min,

Wei

2024

Transactions of the Institute of Measurement and Control

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In this paper, we investigate the state-feedback control for nonlinear systems with a prescribed upper bound of the settling time. First, a prescribed-time stability theorem using Lyapunov analysis and a uniformly bounded scaling function is proposed. Then, based on this theorem and the backstepping technique, a finite-time control procedure is provided for the uncertain nonlinear systems with unmatched nonlinear terms. Based on the design procedure, a state-feedback controller is obtained, which can render the system states exactly convergent to zero in a prescribed time and maintain at zero thereafter. Finally, simulation examples are used to demonstrate the effectiveness of the proposed scheme.

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Fixed-Time Adaptive Neural Network Control for Nonlinear Systems With Input Saturation

Cited by 56 publications

References 47 publications

Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Finite time adaptive neural intermittent control for a class of nonlinear dynamical systems

Prescribed-time state-feedback stabilization of nonlinear systems with unmatched nonlinear terms

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