This article investigates an adaptive prescribed performance path-following control algorithm for rotor-assisted vehicles, incorporating reinforcement learning (RL) to execute energy-saving cruising missions. For obtaining a high-performance path-following controller, a concise prescribed performance control (PPC) algorithm is designed to tightly constrain the output errors within the defined boundaries, while a shifting function is introduced to solve the problem of initial condition restrictions. Furthermore, through integrating the Backstepping method and the optimal control technique, an improved RL with the form of actor–critic neural networks (AC-NNs) is proposed to offer an innovative approach to the challenges of the model uncertainties and external disturbances. In this approach, the actor NN is employed to create an appropriate control policy, while the critic NN is aimed at evaluating the cost-to-go function to modify the system action. Semi-global uniform ultimate bounded (SGUUB) stable properties of the proposed algorithm are guaranteed via the Lyapunov theory. Finally, the superiority and feasibility of the proposed algorithm are verified by two numerical experiments.

This paper proposes an enhanced event-triggered optimal control scheme integrated with reinforcement learning (RL) for wind-assisted autonomous surface vehicles (WAASVs), aiming to ensure the safety and energy efficiency of marine path-following missions. To address the uncertainties arising from the dynamic model and time-varying external disturbances, a reinforcement learning approach based on the architecture of actor–critic neural networks (AC-NNs) is employed to generate control signals without relying on precise model knowledge while minimizing path deviation and energy consumption. Furthermore, an integral event-triggered control (IETC) algorithm is developed to dynamically adjust the control signal updates according to the system output errors, which offers a promising solution to prevent excessive mechanical wear of the actuators. The stability of all error variables is rigorously analyzed using the Lyapunov theory. Finally, two simulation experiments on the rotor-assisted vehicle are performed to validate the superior tracking performance and practical applicability of the proposed algorithm.