Command filtered adaptive output feedback design with novel Lyapunov-based analysis for nonlinear systems with unmodeled dynamics

Tang, Liqiang; Ding, Zegang; Zou, Wencheng; Yang, Yongliang

doi:10.1016/j.jfranklin.2022.07.002

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Remark The Gaussian function in (4) is utilized in References 10,11,15,16,19 as the basis function for RBF neural networks which are capable of approximating any continuous functions over a compact set to arbitrary accuracy. Apart from Gaussian functions, Chebyshev polynomials can also be used as basis functions in Reference 43.…”

Section: Rbfnns and Preliminariesmentioning

confidence: 99%

“…To mention a few for example, in Reference 14 an adaptive CFB tracking control law was proposed for strict‐feedback nonlinear systems with parametric uncertainty. The tracking control for strict‐feedback nonlinear systems with unmodeled dynamics was addressed in References 15 and 16, where adaptive tracking control laws were constructed by combining NNs with CFB. In References 17 and 18, a high‐order fully actuated system approach was combined with the CFB technique to construct tracking control laws for strict‐feedback systems.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 19, the Nussbaum‐based design method was integrated with CFB to address the tracking control problem for nonlinear strict‐feedback systems with multiple unknown time‐varying high‐frequency gains. However, the tracking control results in References 13–19 were obtained based on conventional Lyapunov stability theorem, and thus the tracking error approaches to the origin or an adjustable region around origin asymptotically or exponentially.…”

Section: Introductionmentioning

confidence: 99%

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Fixed‐time adaptive fault‐tolerant tracking control for uncertain strict‐feedback nonlinear systems via command filtered backstepping

Qi,

2024

Intl J Robust & Nonlinear

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In this article, the fault‐tolerant tracking control is addressed for uncertain strict‐feedback nonlinear systems with actuator faults. Neural networks are utilized to identify unknown dynamics in strict‐feedback nonlinear systems, and the adaptive technique is employed to estimate the parameter of actuator effectiveness. More importantly, a command filtered backstepping control method is improved by introducing a fixed‐time command filter and modifying virtual control laws with compensation mechanisms. By incorporating the adaptive neural networks into the command filtered backstepping design framework, a novel adaptive fault‐tolerant control law is constructed. Under the presented control law, the negative influence of the actuator fault and unknown dynamics is effectively compensated simultaneously. Besides, the “explosion of complexity” and “singularity” problems of backstepping is avoided. Moreover, the practical fixed‐time stability is guaranteed for the resulted closed‐loop system.

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Section: Rbfnns and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fixed‐time adaptive fault‐tolerant tracking control for uncertain strict‐feedback nonlinear systems via command filtered backstepping

Qi,

2024

Intl J Robust & Nonlinear

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“…On the basis of DSC, error compensation signals were introduced, which was called command filtered backstepping. In References 21–25, the application of this method could solve the complexity explosion problem as well as avoiding the process of verifying the boundedness of filtering error. Beyond that, event‐triggered control (ETC) had been used and studied by many people in References 26 and 27 so as to reduce the communication burden of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Command filter‐based adaptive event‐triggered control for switched nonlinear systems with full state constraints

Zhang,

Feng

2023

Intl J Robust & Nonlinear

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An adaptive event‐triggered control issue based on command filter and dynamic surface control is studied for switched nonlinear systems with full state time‐varying constraints in this article. To handle state constraints, the nonlinear mapping conversion technique is used. Radial basis function neural networks (RBFNNs) are utilized to approximate unknown nonlinear continuous functions. Adaptive control algorithm is designed based on command filtered backstepping technology. Novel even triggered control is constructed for two different cases in the switching interval. Furthermore, all signals in the switched system are proved to be semi‐globally uniformly ultimate bounded (SGUUB) by the aid of dynamic surface control method under arbitrary switching. Meanwhile, all states do not violate the preset constraints and Zeno phenomenon is avoided. The validity of the presented approach is confirmed through a numerical simulation result.

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Fixed‐time adaptive neural network tracking control for output‐constrained high‐order systems using command filtered strategy

Chen,

Tang,

Ling

2024

Adaptive Control & Signal

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SummaryThis article proposes a fixed‐time adaptive neural command filtered controller for a category of high‐order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: (i) a fixed‐time control framework is extended to the tracking control problem of high‐order systems. (ii) The error compensation mechanism eliminates filter errors that arise from dynamic controllers. (iii) Growth assumptions about unknown functions are relaxed with the help of adaptive neural networks. (iv) More general systems: the developed controller can apply to high‐order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi‐globally uniformly ultimately bounded, and the convergence rate of tracking error is independent of initial conditions. Finally, simulation results validate the advantages and efficacy of the developed control scheme.

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Command filtered adaptive output feedback design with novel Lyapunov-based analysis for nonlinear systems with unmodeled dynamics

Cited by 7 publications

References 48 publications

Fixed‐time adaptive fault‐tolerant tracking control for uncertain strict‐feedback nonlinear systems via command filtered backstepping

Fixed‐time adaptive fault‐tolerant tracking control for uncertain strict‐feedback nonlinear systems via command filtered backstepping

Command filter‐based adaptive event‐triggered control for switched nonlinear systems with full state constraints

Fixed‐time adaptive neural network tracking control for output‐constrained high‐order systems using command filtered strategy

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