Observer-based adaptive neural sliding mode trajectory tracking control for remotely operated vehicles with thruster constraints

• Published in: Transactions of the Institute of Measurement and Control Vol. 43; no. 13; pp. 2960 - 2971
• Main Authors: Chu, Zhenzhong, Chen, Yunsai, Zhu, Daqi, Zhang, Mingjun
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-09-2021; Sage Publications Ltd
• Subjects: Adaptive control; Neural networks; Nonlinearity; Radial basis function; Remote control; Remotely operated vehicles; Saturation; Sliding mode control; State observers; Tracking control; Tracking errors; Trajectory control; observer; adaptive control; sliding mode control; constraints; Remotely operated vehicle
• ISSN: 0142-3312; 1477-0369
• DOI: 10.1177/01423312211004819

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations.