The Improvement And Simulation Research Of Dynamic Matrix Control Algorithm

Model Predictive Control is a type of computer control algorithm developed from the beginning of 1980s. The Dynamic Matrix Control is an important typical algorithm of the predictive control. Concerned with the dynamic matrix control algorithm, this thesis mainly presents the following improvements:1 The expected attenuation factor is introduced into the system control, which improves the constraints of the controller. At the same time, error change rate is also taken into consideration when the feedback correction is designed. In this way, the new predictive control model can predict the system output more accurate. Simulations are carried out on a first-order system, second-order systems, high-end systems and non-minimum phase systems and delay systems. The simulation results comparison are done with the classic Dynamic Matrix Control algorithm.2 In the real applications, the systems are usually with noise. This thesis conducts a further simulation contrast to the noise pollution system. It studies the stability and accuracy of improved Dynamic matrix algorithm and general dynamic matrix control algorithm in such a system environment. Simulation results show that the improved dynamic matrix control algorithm's effect is better than the general dynamic matrix control algorithm's. Its dependence on parameters is not sensitive, so facilitates the selection of parameters. And then it has a stronger anti-noise ability. What is the most worth noticing is that the target system control strategy obtained by improved dynamic matrix control algorithm has a faster convergence speed and stability than the control law obtained by general dynamic matrix control algorithm. Therefore, in the thesis, the improved dynamic matrix control algorithm has better control performance than the general dynamic matrix control algorithm.