Abstract:To enhance the performance and adaptability of trajectory tracking predictive control for omnidirectional mobile robots in dynamic environments and address the limitations of existing machine learning-based parameter tuning methods, such as strong data dependency and the difficulty in balancing short-term control accuracy with long-term system performance, this paper proposed an online self-tuning method for model predictive control (MPC) parameters. This method integrated reinforcement learning theory with an event-triggered mechanism. First, the kinematic model of the omnidirectional mobile robot was established, and a corresponding trajectory tracking MPC framework was constructed. Second, a dynamic parameter optimization framework incorporating the Actor-Critic reinforcement learning was introduced. By designing a reward function that combines state errors and dynamic performance metrics, the controller was driven to optimize control parameters in real time. Furthermore, the event-triggered mechanism was seamlessly integrated into the parameter optimization framework to develop an adaptive controller. This integration reduced the frequency of parameter updates, thereby lowering computational load and enabling efficient control. Finally, a physical experimental platform for omnidirectional mobile robots was developed, and comparative experiments were conducted across multiple scenarios, including step trajectory tracking, Lemniscate curve tracking, and dynamic obstacle avoidance. Experimental results demonstrate that compared to traditional MPC methods using static parameters, the proposed approach reduces overshoot and adjustment time by approximately 70% in step trajectory tracking, decreases state deviation by approximately 65% in Lemniscate trajectory tracking, and reduces state deviation by approximately 30% in dynamic obstacle avoidance scenarios. These results validate the effectiveness and environmental adaptability of the proposed method in enhancing trajectory tracking performance in complex dynamic environments. This research provides novel insights and approaches for addressing the challenges of high-performance trajectory tracking control of mobile robots in dynamic and uncertain conditions.
