Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon
提出一种事件触发模型预测控制策略，通过引入势场和自适应预测时域，在减少计算量的同时实现轮式移动机器人的轨迹跟踪与避障，并分析了系统的稳定性。
This article develops an event-triggered model-predictive control (EMPC) strategy to realize trajectory tracking and obstacle avoidance for a wheeled mobile robot (WMR) subject to input constraints and external disturbances. In the EMPC strategy, a potential field is introduced in the cost function to guarantee a smooth path for the WMR. An event-triggered mechanism is designed to reduce the computational load of solving an optimal control problem (OCP). Moreover, an adaptive prediction horizon is utilized to further achieve computation reduction. Both recursive feasibility of the OCP and practical stability of the resulting closed-loop system are analyzed for the WMR with the input constraints and the external disturbances. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed EMPC strategy.