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We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive–evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment. Full article

Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced. Full article