Proportional-integral-derivative (PID) control is the most widely used in industrial control, robot control and other fields. However, traditional PID control is not competent when the system cannot be accurately modeled and the operating environment is variable in real time. To tackle these problems, we propose a self-adaptive model-free SAC-PID control approach based on reinforcement learning for automatic control of mobile robots. A new hierarchical structure is developed, which includes the upper controller based on soft actor-critic (SAC), one of the most competitive continuous control algorithms, and the lower controller based on incremental PID controller. Soft actor-critic receives the dynamic information of the mobile robot as input, and simultaneously outputs the optimal parameters of incremental PID controllers to compensate for the error between the path and the mobile robot in real time. In addition, the combination of 24-neighborhood method and polynomial fitting is developed to improve the adaptability of SAC-PID control method to complex environments. The effectiveness of the SAC-PID control method is verified with several different difficulty paths both on Gazebo and real mecanum mobile robot. Futhermore, compared with fuzzy PID control, the SAC-PID method has merits of strong robustness, generalization and real-time performance.
Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots
Department of Computer Information Systems, Collage of Engineering and Computer Science, University of Michigan-Dearborn（计算机信息系统系，工程与计算机科学学院，密歇根大学迪尔伯恩分校）；Department of Electrical and Computer Engineering, Collage of Engineering and Computer Science, University of Michigan-Dearborn（电气与计算机工程系，工程与计算机科学学院，密歇根大学迪尔伯恩分校）
Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots
Technological University of Uruguay（乌拉圭技术大学）；Federal University of Rio Grande - FURG（里奥格兰德联邦大学（FURG））；Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学（UFSM））
Autonomous Manipulation of Hazardous Chemicals and Delicate Objects in a Self-Driving Laboratory: A Sliding Mode Approach
Department of Electronic Systems, Aalborg University（电子系统系，奥尔堡大学）；Department of Information Technology and Electrical Engineering, University of Naples（信息科技与电气工程系，那不勒斯大学）
organization= Advanced Research Lab for Control；Agricultural Robotics (Sharif AgRoLab) Department of Mechanical Engineering, Sharif University of Technology , city= Tehran , country= Iran；Robotics Research Group Saxion University of Applied Science , city= Enschede , country= Netherlands
Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、国家科学研究中心、波尔多国立理工学院、LaBRI、UMR 5800）；School of Computer Science, University of Nottingham, UK（nottingham大学计算机科学学院）；Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza（阿拉贡工程研究所（I3A）、 Zaragoza大学）
High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning
Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University（东莞智能装备与智能产业重点实验室，大湾区大学先进工程学院）；Technische Universität Dresden（德累斯顿工业大学）；School of Mechanical and Electrical Engineering, Guangzhou University（广州大学机电工程学院）；University of Science and Technology of China（中国科学技术大学）；College of Computer Science, Dongguan University of Technology（东莞理工学院计算机科学与技术学院）
