Disturbance observer-based prescribed performance super-twisting sliding mode control for autonomous surface vessels

Reinforcement learning-based trajectory tracking optimal control of unmanned surface vehicles in narrow water areas

For unmanned surface vehicles (USVs) navigating in narrow water areas in the presence of unknown dynamics and ocean environmental disturbances, this paper develops a reinforcement learning (RL)-based optimal control scheme for the trajectory tracking of USVs under motion state constraints. A nonlinear map is introduced to transform constrained motion state errors into bounded transformed errors, and then the motion state-constrained trajectory tracking problem of USVs is equivalently transformed into a boundedness problem of the transformed errors. Furthermore, an actor-critic framework is developed by utilizing adaptive neural networks (NNs). Within the actor-critic framework, a novel weight update law is designed for the critic NN by combining the gradient descent approach and the concurrent learning technology, thereby relaxing the persistent excitation condition required for adaptive critic NN weight updates. Subsequently, a disturbance compensator is designed and combined with the actor-critic framework to learn the trajectory tracking optimal control law for USVs in the presence of unknown dynamics and disturbances. Finally, theoretical analyses prove that the developed control scheme guarantees the boundedness of all signals in the USV closed-loop trajectory tracking control system, and simulation results show that the developed control scheme can make USVs track the desired trajectory in narrow water areas while reducing the energy consumption by approximately 14.6 % compared with an existing controller.

A finite-time path following scheme of unmanned surface vessels with an optimization strategy

The path-following problem of unmanned surface vessels (USVs) is solved in this paper, considering unknown interference, energy-saving strategy, input constraints, and input rate of change constraints. Firstly, the USV model considering lumped interference is given, while an interference observer based on finite-time theory is used for interference estimation where the convergence time is specified. Then, to accelerate the USV convergence to a given path, a novel improved adaptive finite-time line-of-sight (IAFTLOS) scheme is developed. The proposed IAFTLOS scheme makes the cross-path error with finite-time convergence. At the same time, an adaptive time-varying looking distance law is designed to improve the convergence performance further. Next, nonlinear model predictive control is introduced to deal with rates of change and input constraints. In addition, an energy-saving strategy is incorporated into the design of the controller. Finally, stable control of the whole cascade system is achieved, and comparative simulation cases verify the feasibility and robustness of the entire system.