Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems

Lin, Guohuai; Li, Hongyi; Ahn, Choon Ki; Yao, Deyin

doi:10.1109/tnnls.2022.3166531

Cited by 147 publications

(40 citation statements)

References 57 publications

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“…However, the solution of the HJB equation is hard to acquire owing to its strong nonlinearity. To solve such a problem, some fuzzy logic systems or neural networks (NNs) approaches have been studied in References 7‐15. To name a few, by applying the online RL algorithm to backstepping control method, Wen et al in Reference 12 developed an optimal backstepping control strategy for the nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Yang

Pan

Sun

et al. 2022

Intl J Robust & Nonlinear

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In this article, the event‐triggered optimized adaptive tracking control problem is investigated for a class of multi‐agent systems subject to full‐state constraints. To address the full‐state constraints problem, a nonlinear mapping technique is applied, which can release the feasibility conditions for virtual controllers. Based on the mean‐value theorem, the nonaffine nonlinear terms generated by transformed system are separated, which overcomes the obstacle of solving optimized solution. The neural network based reinforcement learning (RL) algorithm with the identifier‐critic‐actor architecture is introduced to obtain the optimal solution of systems with unknown dynamics. It is worth noting that the RL algorithm in this article is simplified, which can reduce the computational burden. To reducing the communication burden, an event‐triggered mechanism with time‐varying threshold related to the optimized control signal is developed. By applying the Lyapunov stability method, it is proved that the desired optimized tracking performance and the stability of the closed‐loop systems can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.

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Section: Introductionmentioning

confidence: 99%

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Yang

Pan

Sun

et al. 2022

Intl J Robust & Nonlinear

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“…Recently, the finite‐time and fixed‐time control methods were proposed successively in References 31 and 32, both of which can guarantee the controlled systems reach stability rapidly. The latter control method has the advantage that the convergence time of dynamic systems is irrelevant to the initial value of the systems.…”

Section: Introductionmentioning

confidence: 99%

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

Hao

Pan

Zhu

et al. 2022

Intl J Robust & Nonlinear

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This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.

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“…The multiagent systems (MASs) 1,2 have developed rapidly in a large amount of fields, such as cooperative surveillance, 3 multivehicle cooperative control, 4 unmanned aerial vehicle, 5 and underwater vehicles. 6 As a basic problem of cooperative control, 7 the tracking control for MASs has been widely studied.…”

Section: Introductionmentioning

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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Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems

Cited by 147 publications

References 57 publications

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

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

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