具有指定收敛速度的离散系统鲁棒跟踪数据驱动设计

A Data-Driven Prescribed Convergence Rate Design for Robust Tracking of Discrete-Time Systems

本文研究了具有指定收敛速度的线性离散时间系统鲁棒跟踪设计问题。首先利用鲁棒输出调节理论描述了跟踪控制问题,再结合系统数据与强化学习实现了具有指定收敛速度的跟踪控制。学习得到的控制方案不仅保证了跟踪误差渐近收敛到零,而且具有针对不确定系统动态的鲁棒性。本文所述的指定收敛速度设计不依赖系统演化时间或精确系统模型,因此是数据驱动的。

A robust tracking control problem for linear discrete-time systems with a prescribed convergence rate is considered.The robust tracking problem is formulated by utilizing robust output regulation and is subsequently solved by reinforcement learning with integration of the prescribed convergence rate.The learned controller ensures that the tracking error asymptotically converges to zero,meanwhile it is robust to uncertain system dynamics.The proposed convergence rate design is data-driven in the sense that it does not depend on the time for the system evolution or the accurate system model.