Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Jin Xin, Wang Yue, Luo Lin
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引用次数: 1

Abstract

This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.
基于动态PLS框架的无偏移跟踪预测控制
提出了一种基于动态偏最小二乘框架的无偏置跟踪模型预测控制方法。首先,采用状态空间模型作为PLS的内部模型来描述动态系统,并采用子空间识别方法对内部模型进行识别。在此基础上,设计了多个独立模型预测控制(MPC)控制器。由于PLS的解耦特性,这些控制器是分开运行的,适合于分布式控制框架。此外,MPC的代价函数中考虑了内部模型输出的增量,这涉及到控制器的积分作用。因此,保证了无偏移跟踪性能。工业背景仿真结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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