Jul 2026· International Journal of Dynamics and Control· Vol 14· 0 citations· 54 references
TL;DR
Simulation results demonstrate that the proposed controller accurately drives the underactuated AUV to follow the desired course despite ED and IC, and it also shows clear advantages over other comparison methods.
The DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers is introduced, which achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference condi...
Ling Wang, Yan Shi· SAE technical paper series· 0 citations
Simulation results for spacecraft landing demonstrate that the proposed method achieves near-optimal performance while maintaining provable stability throughout the descent.
This work presents a hybrid learning-control architecture that integrates a DS-based IL reference generator with a neuroadaptive controller, enabling dynamic human-like reactive alignment-termed behavioral tracking under persistent marine perturbations.
Yeyson A. Becerra-Mora, José Ángel Acosta· 0 citations
We present NST-RL, a framework that couples residual reinforcement learning (RL) with an approximate optimal regulator computed using Nonlinear Systems Toolbox (NST) for robust control. Disturbance compensation is learned through residual corrections routed via a dynamic extension that preserves control-input positivit...
Guining Pertin, D. Chang· IEEE Robotics and Automation...· 0 citations
Autonomous driving has the potential to greatly enhance traffic efficiency, and its effectiveness depends on robust decision-making in complex real-world environments. As an emerging technique, Deep Reinforcement Learning (DRL) is expected to address this requirement. However, most existing general-purpose DRL methods...
Rui Guo, Xin-Yu Li, Zhong-Hao Fu et al.· IEEE Open Journal of Intelli...· 0 citations
This review aims to clarify the development path of DRL-driven robot adaptive control, offering reference for theory, algorithm design, and engineering practice and it is hoped that this survey will help new researchers quickly grasp the landscape of the field.