Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault

Wang, Zhanshan; Zhang, Huaguang

doi:10.1109/tsmc.2017.2672664

Cited by 137 publications

(60 citation statements)

References 57 publications

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“…∇ The proposed sliding-mode controller (16) with F 0 defined in (17), can drive the error dynamics (10)- (11) to the sliding surface (13) in finite time. Since the sliding motion has been asymptotically stable as analysed earlier, it follows that lim t→∞ e 1 (t) = 0 and lim t→∞ e 2 (t) = 0.…”

Section: Casementioning

confidence: 99%

“…where Γ m is a positive constant,θ ν (t) andρ ν (t) are give in (32) and (33), F 0 (t) is defined in (17), m and d b are given in (5), r(t) is a nonnegative time varying gain, and g m (t) is given as…”

Section: Remarkmentioning

confidence: 99%

“…where ϑ(t) is the bounded time-varying actuator fault, and |ϑ(t)| ≤ ϑ 1 with ϑ 1 unknown. The fault-tolerant control law is proposed to be 8 whereθ ν (t) andρ ν (t) are the estimates of ϑ * ν = ϑ1 σν and ρ * ν = 1 σν , respectively, F 0 (t) is defined in (17), v(t) is a design signal, and r(t) is a nonnegative time varying gain.…”

Section: Extension To the Unparameterized Fault Casementioning

confidence: 99%

See 2 more Smart Citations

Adaptive Fault-Tolerant Sliding-Mode Control for High-Speed Trains With Actuator Faults and Uncertainties

Mao

Yan

Zhang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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In this paper, a novel adaptive fault-tolerant slidingmode control scheme is proposed for high-speed trains, where the longitudinal dynamical model is focused, and the disturbances and actuator faults are considered. Considering the disturbances in traction force generated by the traction system, a dynamic model with actuator uncertainties modelled as input distribution matrix uncertainty is established. Then, a new sliding-mode controller with design conditions is proposed for the healthy train system, which can drive the tracking error dynamical system to a pre-designed sliding surface in finite time and maintain the sliding motion on it thereafter. In order to deal with the actuator uncertainties and unknown faults simultaneously, the adaptive technique is combined with the fault-tolerant slidingmode control design together to guarantee that the asymptotical convergence of the tracking errors is achieved. Furthermore, the proposed adaptive fault-tolerant sliding-mode control scheme is extended to the cases of the actuator uncertainties with unknown bounds and the unparameterized actuator faults. Finally, case studies on a real train dynamic model are presented to explain the developed fault-tolerant control scheme. Simulation results show the effectiveness and feasibility of the proposed method.Index Terms-Actuator faults, fault-tolerant sliding-mode control, adaptive control, actuator uncertainty, high-speed train.

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Section: Casementioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Extension To the Unparameterized Fault Casementioning

confidence: 99%

See 1 more Smart Citation

Adaptive Fault-Tolerant Sliding-Mode Control for High-Speed Trains With Actuator Faults and Uncertainties

Mao

Yan

Zhang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…The method proposed in [34] to justify the positive definiteness of the Hessian matrix of (9) is computationally very demanding and, for large systems, it is actually not possible to decide whether E 1 is a Lyapunov function. This is the main weakness of the "open-loop" analysis framework and several other energy functions considered in the literature and the main motivation for proposing (6). In the remainder of the paper, we will study the nonlinear excitation control synthesis and analyze the stability of the closed-loop power system based on the Lyapunov-like energy function E given in (6).…”

Section: A Structure Preserving Energy Functionmentioning

confidence: 99%

“…By utilizing such simplified models, a number of control technologies are presented to design effective excitation controllers [5]. In fact, in the realistic applications, the more accurate the model is, the better performance the designed control system has, but also the more challenges one needs to address [6]. RNM and SPM are two types of power system models for the excitation control problem.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Adaptive Excitation Control for Structure Preserving Power Systems

Wan

Milano

2018

IEEE Trans. Power Syst.

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The paper proposes a decentralized excitation controller to improve the stability of large power systems. Extended Lyapunov-like energy function and equivalent expressions of stator transient voltages are utilized to design a nonlinear adaptive excitation controller. The proposed controller only requires local machine measures and parameters. The performance of the designed excitation control system is evaluated on the wellknown IEEE 118-bus 54-unit power system and compared with conventional exciter controls. Index Terms-Multi-machine power systems, automatic voltage control, structure preserving model, transient stability analysis, decentralized control, Lyapunov function. NOMENCLATURE SPM Structure preserving model. RNM Reduced-network model. AEC Adaptive excitation controller. RNC RNM-based excitation controller. m Number of machines. b Number of buses. l, k, pjq Index of (machine) bus. Ω l Index set of buses which connect to bus l. Ω Index set of branches, k P Ω l ñ lk P Ω. b, pmq Index set of (machine) buses,m Ďb. Z 0 Equilibrium value of any variable Z. 9 Z Time derivative of any variable Z. Hessp¨q Hessian matrix of a function. V t l Voltage magnitude of bus l, in pu. V Voltage magnitude vector. θ l Voltage angle of bus l, in rad. θ Voltage angle vector. δ j Power angle of jth machine, in rad. δ Power angle vector. δ j , θ lk Relative anglesδ j " δ j´θj , θ lk " θ l´θk. V dj , V qj V dj "´V tj sinδ j , V qj " V tj cosδ j. V sj ,Ṽ cjṼsj " V dj0´Vdj ,Ṽ cj " V qj´Vqj0. ω j Relative speed of jth machine, in rad/s. ω Relative speed vector.

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Design Scheme III of FTZNN

2022

Zeroing Neural Networks

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Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault

Cited by 137 publications

References 57 publications

Adaptive Fault-Tolerant Sliding-Mode Control for High-Speed Trains With Actuator Faults and Uncertainties

Adaptive Fault-Tolerant Sliding-Mode Control for High-Speed Trains With Actuator Faults and Uncertainties

Nonlinear Adaptive Excitation Control for Structure Preserving Power Systems

Design Scheme III of FTZNN

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