NN-Based Fixed-Time Attitude Tracking Control for Multiple Unmanned Aerial Vehicles with Nonlinear Faults

Zheng, Xiaohong; Li, Hongyi; Ahn, Choon Ki; Yao, Deyin

doi:10.1109/taes.2022.3205566

Cited by 88 publications

(29 citation statements)

References 60 publications

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“…In the future, fault detection will become a hot topic and the direction of further work. Our future work is to investigate more complex situations such as References 42‐44.…”

Section: Discussionmentioning

confidence: 99%

Compound conditions event‐triggered tracking control for nonlinear multiagent systems with nonlinear output faults

Xue

Cao

2022

Adaptive Control & Signal

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Summary This article is devoted to investigating the compound conditions event‐triggered prescribed performance tracking problem for strict‐feedback nonlinear multiagent systems with nonlinear output faults and unknown disturbances. A simplified event‐triggered sampling condition is designed to successfully reduce the number of state triggered design parameters. Besides, a compound conditions event‐triggered mechanism is constructed, which integrates state triggered and controller triggered simultaneously to reduce communication burden. Furthermore, by using disturbance observers and prescribed performance functions, unknown external disturbances are compensated and the tracking errors can converge into the prescribed boundary, respectively. Moreover, all the signals of the closed‐loop system are semiglobally uniformly ultimately bounded. Finally, the availability of the proposed control strategy is testified via simulation results.

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“…In the future, fault detection will become a hot topic and the direction of further work. Our future work is to investigate more complex situations such as References 42‐44.…”

Section: Discussionmentioning

confidence: 99%

Compound conditions event‐triggered tracking control for nonlinear multiagent systems with nonlinear output faults

Xue

Cao

2022

Adaptive Control & Signal

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“…In recent years, the problem of finite-time stability has been widely concerned and many methods have been developed in the literatures. [16][17][18][19][20][21][22][23] For instance, the problem of the finite-time tracking control was studied in Reference 16. The proposed control method can guarantee that the system states in the finite time converge to the origin in Reference 17.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the problem of finite‐time stability has been widely concerned and many methods have been developed in the literatures 16‐23 . For instance, the problem of the finite‐time tracking control was studied in Reference 16.…”

Section: Introductionmentioning

confidence: 99%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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Summary This paper proposes a self‐triggered (ST) adaptive prescribed‐time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one‐to‐one mapping and backstepping technique, an adaptive prescribed‐time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed‐loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.

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“…In recent several decades, NN-based techniques have gained great attention. The NNs are used as observers in [27], [28], adaptive NN is used to approximate unknown uncertainties in the system's dynamics [7], [21], [29], [30], and a variety of different types of nonlinear systems have been explored by using NNs-based adaptive backstepping techniques to deal with unknown nonlinearities [31]- [35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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NN-Based Fixed-Time Attitude Tracking Control for Multiple Unmanned Aerial Vehicles with Nonlinear Faults

Cited by 88 publications

References 60 publications

Compound conditions event‐triggered tracking control for nonlinear multiagent systems with nonlinear output faults

Compound conditions event‐triggered tracking control for nonlinear multiagent systems with nonlinear output faults

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

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