状态空间模型的双层结构预测控制算法

Double-layered model predictive control of state-space model

中文关键词  预测控制  状态空间  Kalman滤波  设定值优化  双层结构

英文关键词  model predictive control (MPC)  state-space  Kalman filter  setpoint optimization  double-layered structure

中文摘要

      双层结构预测控制是指先进行设定值优化、再进行设定值跟踪的预测控制. 在已有的双层结构动态矩阵控制的 基础上, 本文给出基于状态空间模型的双层结构预测控制算法. 该算法基于干扰模型和新定义的开环预测值, 给出了新 的开环预测模块. 该开环预测模块采用Kalman滤波方法得到操作变量、被控变量的开环动、稳态预测值. 基于这些开环 预测值, 稳态目标计算模块的基本原理同双层结构动态矩阵控制, 但是具体细节上遵循状态空间方法. 动态控制模块基 于稳态目标计算提供的操作变量、被控变量的稳态目标(设定值), 采用二次规划算法计算控制作用. 仿真算例证实了该 算法的有效性.

英文摘要

      The so-called double-layered model predictive control (MPC) performs firstly the setpoint optimization, then the setpoint tracking. Based-on the existing double-layered dynamic matrix control, this paper gives an algorithm for double-layered MPC based on the state-space model. Based on the disturbance model and the newly defined open-loop predictions, this algorithm proposes a new open-loop prediction module. This open-loop prediction module adopts the Kalman filter to obtain the open-loop dynamic/steady-state predictions of manipulated/controlled variables (MVs/CVs). Based on these open-loop predictions, the steady-state target calculation (SSTC) module is the same as in double-layered dynamic matrix control, but its details obey the state-space method. Based on the steady-state targets (setpoints) of MVs/CVs provided by SSTC, the dynamic control module computes the control moves by solving the quadratic programming. The numerical example verifies the effectiveness of the proposed algorithm.