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动态矩阵控制(DMC)是模型预测控制的一种典型算法,它以被控对象的单位阶跃响应为预测和控制的模型,通过在线滚动优化和反馈校正来实施优化控制。对于存在状态等式约束的线性系统,文中在解决系统状态重构问题时改进了传统观测器,构造了投影卡尔曼滤波器,使系统的状态更好的符合约束条件,增强系统的稳定性。同时与概率约束算法进行比较,Matlab仿真结果证明了其优越性。
Dynamic matrix control (DMC) is a typical model predictive control algorithm. It takes the unit step response of the controlled object as the model of prediction and control, and optimizes the control by online rolling optimization and feedback correction. For the linear systems with state equality constraints, the traditional observer is improved and the projection Kalman filter is improved to solve the system state reconstruction problem, so that the state of the system can better meet the constraints and enhance the stability of the system. At the same time, compared with the probability constraint algorithm, the result of Matlab simulation proves its superiority.