基于状态空间模型的双时间尺度预测控制算法

A two-time scale model predictive control algorithm based on state-space model

多时间尺度问题在控制领域已得到广泛关注。针对多时间尺度问题的典型代表——双时间尺度问题,学者们已提出多种控制算法来分析处理。这些控制算法大多是根据奇异摄动法将控制系统分解为快、慢两个独立的子系统,并对子系统分别进行优化求解,这类算法往往忽略子系统之间控制与输出的耦合作用。本文在上述控制算法的基础上,提出一种考虑快、慢子系统双方控制与输出间的耦合作用的双时间尺度预测控制算法。该算法以系统原状态空间模型分解得到的快、慢两个子系统模型为被控对象,将两个模型的预测值信息整合到同一预测控制优化问题中,实现对整个系统的控制。仿真实例验证了该方法的有效性。

Multi-time scale problems are received widely attention in the field of control and two-time scale problem, as the typical representative of multi-time scale problem, some scholars have proposed a variety of control algorithm to analysis and process these problem. Most of these control algorithms are based on singular perturbation method which decomposes the control system into ‘fast＇ and ‘slow＇ separated subsystems, and optimize the subsystems respectively. But these kinds of algorithms ignore the coupling between the control actions and outputs of these subsystems. On the basis of the above algorithms, this paper presents a kind of double-rate predictive control algorithm which considers the coupling between the control actions and outputs of these two models. The algorithm treats the ‘fast＇ and ‘slow＇ subsystems model which are decomposed from the state-space model of the original system as the controlled objects and integrates the forecast information of the two model into one predictive control optimization problem to realize the control of the whole system. A simulation example is given to illustrate the effectiveness.