TL;DR
Simulation results show that the IRL actor–critic controller matches or improves upon the fixed-gain baseline after a plant change while, unlike the certainty-equivalence scheme, requiring no continuous probing/dither signal for identifiability—avoiding the associated persistent tracking-error penalty.
This paper presents a reinforcement learning (RL)-based trajectory tracking control method for an autonomous vehicle (AV) under coupled road disturbances and denial-of-service (DoS) attacks. Unlike existing studies that treat physical-layer disturbances and network-layer attacks separately, we construct a unified augme...
This paper develops a certified imitation-learning framework for approximating model predictive control (MPC) policies with feedforward neural controllers and validates it on autonomous-vehicle lateral control. An exact finite-horizon Q-loss is constructed by fixing the learner's first steering action in the expert MPC...
This research introduces an innovative control technique for Series Elastic Actuators (SEAs) that utilizes Reinforcement Learning (RL) to address the shortcomings of previously fixed-gain adaptive controllers, which are a hybrid of State Feedback Control (SFC) and Model Reference Adaptive Control (MRAC) by using Lyapun...
Traditional fixed-gain proportional–integral–derivative (PID) controllers often exhibit limited adaptability when regulating the longitudinal motion of passenger aircraft under varying flight conditions and external disturbances. This study proposes an adaptive control framework that integrates reinforcement learning (...
Comparative simulations on a 2-DOF planar manipulator demonstrate that the proposed method provides faster convergence and improved steady-state tracking accuracy than both a PID baseline and a classical Slotine–Li adaptive baseline, while respecting the predefined-time bound.
This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...
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