This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm for feedback linearizable systems with experimental validation on a mobile robot. Traditional feedback and feedforward controllers are designed based on the nominal model by using Feedback Linearization Control (FLC) method. Then, an intelligent controller is designed based on sliding mode learning algorithm that utilizes closed-loop error dynamics to learn the system behavior. The controllers are working in parallel, and the intelligent controller can gradually replace the feedback controller from the control of the system. In addition to the stability of the sliding mode learning algorithm, the closed-loop stability of an $n$th order feedback linearizable system is proven. The simulation results demonstrate that CLELC algorithm can improve control performance (e.g., smaller rise time, settling time and overshoot) in the absence of uncertainties, and also provides robust control performance in the presence of uncertainties as compared to traditional FLC method. To test the efficiency and efficacy of CLELC algorithm, the trajectory tracking problem of a tracked mobile robot is studied in real-time. The experimental results demonstrate that CLELC algorithm ensures high-accurate trajectory tracking performance than traditional FLC method.
SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version
NASA-JPL, Caltech（NASA-喷气推进中心，加州理工学院）；Dept. of Aerospace Eng. and Amato is with the Dept. of Computer Science（航空航天工程系和计算机科学系）；Dept. of Computer Science（计算机科学系）
Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
Chalmers University of Technology（查尔姆斯理工大学）；Zenseact AB（Zenseact公司）；IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）；Università di Modena e Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Wasserstein Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Data Science, City University of Hong Kong（香港城市大学数据科学学院）
Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Uncertain Dynamics
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；School of Robotics, Hunan University（机器人学院，湖南大学）
Lyapunov-Based PI-Like Control for Robust Trajectory Tracking of a Four-Wheel Independently Driven and Steered Robot: Design and Experimental Validation
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb（Zagreb大学机械工程与造船工程学院）；Regional Centre of Excellence for Robotic Technology (CRTA)（机器人技术卓越研究中心）；Croatian Academy of Sciences and Arts（克罗地亚科学院）
BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
Department of Mechanical and Aerospace Engineering, University of California, Irvine（加州大学尔湾分校机械与航空航天工程系）；U.S. DEVCOM Army Research Laboratory (ARL)（美国陆军研究实验室）
A Koopman Operator-based NMPC Framework for Mobile Robot Navigation under Uncertainty
Department of Electrical and Computer Engineering, University of California, Riverside（加州大学河滨分校电气与计算机工程系）；Polytechnic School of Tunisia（突尼斯多技术学院）；Institute of AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
Dynamic Motion/Force Control of Mobile Manipulators via Extended-UDE
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation
Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；South China University of Technology（华南理工大学）；University of Essex（埃塞克斯大学）；Instituto de Telecomunicaões（葡萄牙电信研究所）；University of New South Wales（新南威尔士大学）
The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement with On-Orbit Validation
SGT Inc.（SGT公司）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Institute for Systems and Robotics（系统与机器人研究所）；Instituto Superior Técnico（高等技术学院）；Massachusetts Institute of Technology（麻省理工学院）
