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Adaptive Neural Finite-time Trajectory Tracking Control of MSVs Subject to Uncertainties

Qiang Zhang*, Meijuan Zhang, Renming Yang, and Namkyun Im
International Journal of Control, Automation, and Systems, vol. 19, no. 6, pp.2238-2250, 2021

Abstract : This paper provides two finite-time trajectory tracking control schemes for marine surface vessels (MSVs) which are influenced by dynamic uncertainties and unknown time-varying disturbances. Neural networks (NNs) are applied to reconstruct the vehicle’s dynamic uncertainties, and the sum of upper bound of approximation error and external unknown disturbances is estimated by designing an adaptive law. According to the backstepping technique and finite-time stability theory, a finite-time trajectory tracking control scheme is presented. Further, to decrease the conservatism of the presented control scheme caused by estimating the upper bound, a multivariate sliding mode finite-time disturbance observer (MSMFTDO) is designed to estimate the unknown external disturbances and the part that is not completely reconstructed by NNs, and then a MSMFTDO-based adaptive neural finite-time trajectory tracking control law is designed. Rigorous theoretical analyses are provided to prove that, owing to the developed finite-time trajectory tracking control strategies, all the signals of the closed-loop trajectory tracking control system are bounded, and that the actual trajectory of MSVs is able to track the reference trajectory in finite time. Simulation results illustrate the effectiveness of the developed schemes.

Keyword : Adaptive neural network, finite time, marine surface vessel, multivariate sliding mode finite-time disturbance observer, trajectory tracking control.

 
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