A Hierarchical Data-Driven Predictive Control of Image-Based Visual Servoing Systems With Unknown Dynamics
针对轮式移动机器人，提出一种分层预测控制算法，在运动学层用迭代线性二次调节器计算速度，在动力学层用高斯过程数据驱动方法处理未知动力学，并证明了系统的输入到状态实际稳定性。
In this article, a hierarchical predictive control (PC) algorithm is designed for visual servoing mobile robot systems. At the kinematic level, the image-based visual servoing model of a wheeled mobile robot is established. By defining the corresponding performance index of the PC, an iterative linear quadratic regulator (iLQR) is used to obtain the velocity controller and to provide reference velocity for dynamics. In dynamics, a data-driven PC controller based on the Gaussian process (GP) is proposed to obtain the torque controller with unknown dynamics. The input-to-state practical stability (ISpS) of the system based on the proposed data-driven PC method is proved by introducing reasonable assumptions. The corresponding theorem also analyzes the maximum upper bound of GP inference error. Finally, the effectiveness of the proposed hierarchical controller is verified by simulations and experiments.