IEEE/CAA Journal of Automatica Sinica
Citation:  C. Ma and D. Dong, “Finitetime prescribed performance timevarying formation control for secondorder multiagent systems with nonstrict feedback based on a neural network observer,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 1039–1050, Apr. 2024. doi: 10.1109/JAS.2023.123615 
This paper studies the problem of timevarying formation control with finitetime prescribed performance for nonstrict feedback secondorder multiagent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finitetime prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closedloop system has practical finitetime stability and that formation errors of the multiagent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
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