Abstract: Model predictive control (MPC) is a model-based optimal control strategy widely used in robot systems.In this work, the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed. First, two novel path tracking performance indices, i.e., steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second, the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique, and then a novel controller structure which can automatically tune the control parameters online is further designed. Finally, experimental verification with an actual wheeled mobile robot is conducted, which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity, accuracy and adaptability of the robot path tracking.

Key words: model predictive control (MPC), path tracking, mobile robot, machine learning, parameter tuning

摘要： 模型预测控制（MPC）是一种广泛应用于机器人系统的基于模型的最优控制策略。研究了轮式移动机器人路径跟踪MPC控制器参数整定问题，提出了一种新的参数自整定方法。首先，提出了两个新的路径跟踪性能指标，即稳态时间比值和稳态距离比值，以更准确地反映MPC控制性能。其次，基于机器学习方法构建所提出的指标与MPC参数之间的映射关系，进一步设计了一种能够在线自动整定控制参数的新型控制器结构。最后，利用一台真实的轮式移动机器人进行实验验证。实验结果表明：与现有方法比较，所提出方法在机器人路径跟踪的快速性、准确性和自适应性方面都有显著提高。

关键词: 模型预测控制，路径跟踪，移动机器人，机器学习，参数整定