Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

作     者：Nguyen, Minh-Nhat Van, Mien McIlvanna, Stephen Sun, Yuzhu Close, Jack Olayemi, Kabirat Jin, Yan 

作者机构：School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom School of Mechanical and Aerospace Engineering Queen's University Belfast Belfast United Kingdom 

出 版 物：《IEEE Transactions on Intelligent Vehicles》 (IEEE Trans. Intell. Veh.)

年 卷 期：2024年第9卷第11期

页      面：1-12页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 08[工学] 080203[工学-机械设计及理论] 0837[工学-安全科学与工程] 0802[工学-机械工程] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 

基　　金：EPSRC DTP Programme Natural Environment Research Council Royal Society 

主　　题：Mobile robots 

摘      要：Within the context of Nonlinear Model Predictive Control (NMPC) design for autonomous mobile robots, which face challenges such as parametric uncertainty and measurement inaccuracies, focusing on dynamic modelling and parameter identification becomes crucial. This paper presents a novel safety-critical control framework for a mobile robot system that utilises NMPC with a prediction model derived entirely from noisy measurement data. The Sparse Identification of Nonlinear Dynamics (SINDY) is employed to predict the system s state under actuation effects. Meanwhile, the Control Barrier Function (CBF) is integrated into the NMPC as a safety-critical constraint, ensuring obstacle avoidance even when the robot s planned path is significantly distant from these obstacles. The closed-loop system demonstrates Input-to-State Stability (ISS) with respect to the prediction error of the learned model. The proposed framework undergoes exhaustive analysis in three stages, training, prediction, and control, across varying noise levels in the state data. Additionally, validation in Matlab and Gazebo illustrates that the NMPC-SINDY-CBF approach enables smooth, accurate, collision-free movement, even with measurement noise and short prediction times. Our findings, supported by tests conducted with the Husky A200 robot, confirm the approach s applicability in real-time scenarios. IEEE