Neural-networks-based adaptive quantized feedback tracking of uncertain nonlinear strict-feedback systems with unknown time delays

Choi, Yun Ho; Yoo, Sung Jin

doi:10.1016/j.jfranklin.2020.08.046

Cited by 34 publications

(25 citation statements)

References 47 publications

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“…Theorem 1: Consider nonlinear time-delay system (1) under unknown false data injection attacks and actuator faults under Assumption 1-3, the proposed adaptive resilient control scheme, including the FO filter (8), compensation function ( 9), the intermediate control law ( 10)-( 11), the actual control law ( 14) and (15), and the adaptive laws (12), ( 13), (16), and ( 17), can ensure that the following properties hold.…”

Section: A Adaptive Finite-time Resilient Control Designmentioning

confidence: 99%

“…Ma et al [11] addressed the event-triggered adaptive output-feedback control issue of nonstrict-feedback nonlinear systems with time-varying state delays and input delay for the first time, where the ISS assumption was fully removed. Furthermore, the dynamic surface control (DSC) method and the command filtered backstepping technique were employed to avoid the problem of "explosion of complexity" in [12]- [15], which reduced the computational burden greatly. Still, it is worth noting that the aforementioned results only guaranteed the asymptotic stability of the closed-loop system (CLS), which means that the closed-loop convergence can be ensured as time goes to infinity.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Song

Park

Zhang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractionalorder command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

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Section: A Adaptive Finite-time Resilient Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Song

Park

Zhang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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“…Remark 1. Compared with the existing adaptive control works for state-quantized nonlinear systems [22][23][24][25], this paper presents the error transformation method using the auxiliary variable ρ to deal with the input delay problem in the quantized feedback control framework. We design the input delay compensator using quantized states at the last design step and the stability analysis strategy of the total closed-loop system including this input delay compensator is presented in this paper.…”

Section: Adaptive State-quantized Tracking Control In the Presence Of...mentioning

confidence: 99%

“…To approximate unknown and unmatched nonlinearities in nonlinear systems, a neural-network-based quantized feedback control result is presented in [24]. This approach has been extended to nonlinear strict-feedback systems with state delays [25] where the Lyapunov-Krasovskii functional technique is employed to remove the effects of state delays. Despite these studies, the input delay problem has not been investigated in the quantized feedback control framework of triangular nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…The measured full states are quantized by state quantizers and the quantized states are only used to design the controller. Different from the existing quantized feedback control results [22][23][24][25], our primary contribution is to establish an input delay compensation strategy using quantized states in the state-quantized control framework while ensuring the robustness against unknown nonlinearities and external disturbances. To this end, an error coordinated transformation using the auxiliary variable is derived to design the delay compensator and the neural-network-based adaptive controller.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive State-Quantized Control of Uncertain Lower-Triangular Nonlinear Systems with Input Delay

Yoo

2021

Mathematics

Self Cite

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In this paper, we investigate the adaptive state-quantized control problem of uncertain lower-triangular systems with input delay. It is assumed that all state variables are quantized for the feedback control design. The error transformation method using an auxiliary time-varying signal is presented to deal with the compensation problem of input delay. Based on the error surfaces with the auxiliary variable, a neural-network-based adaptive state-quantized control scheme is constructed with the design of the input delay compensator. Different from existing results in the literature, the proposed method exhibits the following features: (i) compensating for the input delay effect by using quantized states; and (ii) establishing the stability of the adaptive quantized feedback control system in the presence of input delay. Furthermore, the boundedness of all the signals in the closed-loop and the convergence of the tracking error are analyzed. The effectiveness of the developed control strategy is demonstrated through the simulation on a hydraulic servo system.

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Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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This article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.

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Neural-networks-based adaptive quantized feedback tracking of uncertain nonlinear strict-feedback systems with unknown time delays

Cited by 34 publications

References 47 publications

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Adaptive State-Quantized Control of Uncertain Lower-Triangular Nonlinear Systems with Input Delay

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

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