This paper proposes a novel model predictive control method with velocity estimation simultaneously constraining trajectory and velocity tracking errors for a cushion robot. The authors investigated a path planning method using improved particle swarm optimization (PSO) combined with Dijkstra’s algorithm and obtained a real-time desired optimal motion path for obstacle avoidance. The authors designed a velocity observer to estimate the unmeasurable speed, while the asymptotic stability of the observer error system was established. A predictive controller with error-constrained performance was derived by solving a quadratic programming problem with incremental control. Simulation and experimental results confirm the effectiveness of the proposed method and verify that the error constraints adopted in the cushion robot provide safe motion while avoiding obstacles.

中文翻译：

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基于PSO的坐垫机器人速度观测器预测控制。

本文提出了一种新的模型预测控制方法，该方法具有速度估计同时约束坐垫机器人的轨迹和速度跟踪误差的优点。作者研究了一种使用改进的粒子群优化（PSO）结合Dijkstra算法的路径规划方法，并获得了用于避开障碍物的实时所需的最佳运动路径。作者设计了一个速度观测器，以估计无法测量的速度，同时建立了观测器误差系统的渐近稳定性。通过解决带有增量控制的二次编程问题，得出了具有错误约束性能的预测控制器。