基于动态参考规划的鲁棒Tube模型预测跟踪控制

Dynamic reference programming-based robust Tube model predictive tracking control

为实现扰动和约束作用下对系统的最优鲁棒跟踪,提出一种动态参考规划(DRP)方法,设计鲁棒Tube模型预测控制器(RTMPC)将系统状态驱动到以最优跟踪点为中心的扰动不变集内.基于DRP的RTMPC控制方法,以多步参考为决策变量,确保在线优化递归可行性的同时,增加在线优化的自由度;另外,通过设定目标函数惩罚标称状态轨迹和参考稳态之间、以及最后一步参考稳态和设定点之间的加权欧式距离,可实现最优鲁棒跟踪.

In order to achieve the goal of optimal robust tracking of the system under perturbations and constraints,a dynamic reference programming(DRP)method is proposed to accomplish the target.Therefore,a method called robust Tube model predictive control(RTMPC)is designed.The RTMPC method is proposed to drive the system state to achieve a disturbance invariant set successfully,which is centered on the optimal tracking point.The RTMPC control method based on DRP takes multi-step reference as the decision variable,which is to ensure the feasibility of online optimization recursion and increase the freedom of online optimization at the same time.In addition,the optimal robust tracking can be achieved by setting an objective function,which is used to punish the weighted Euclidean distance between the nominal state trajectory and the reference steady state,as well as the last step between the reference steady state and the set point.