Event-Triggered Approximate Optimal Path-Following Control for Unmanned Surface Vehicles With State Constraints

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 1; pp. 104 - 118
• Main Authors: Zhou, Weixiang, Fu, Jun, Yan, Huaicheng, Du, Xin, Wang, Yueying, Zhou, Hua
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive dynamic programming (ADP); Algorithms; Artificial neural networks; Backstepping; backstepping control; Closed loops; Computer applications; Constraint modelling; Control algorithms; Control methods; Control theory; Controllers; Cost function; Dynamic programming; event-triggered control; Feedback control; Feedforward control; Guidance (motion); Learning systems; Liapunov functions; Modules; neural network (NN); Neural networks; Nonlinear control; Nonlinear systems; path following; Sea surface; Stability analysis; state constraints; Surface vehicles; Trajectory planning; Uncertainty; unmanned surface vehicle (USV); Unmanned vehicles; Vehicle dynamics
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3090054

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.