基于柯西-反向秃鹫融合算法的优化动态矩阵控制OA

In view of the problem that controller parameters are difficult to select for complex systems with large time delays and large inertia,this study proposes a Cauchy-reverse vulture fusion algorithm to optimize the parameter selection of DMC(Dynamic Matrix Control).By introducing Cauchy mutation,lens opposition-based learning,and the exploration mechanism of the eagle algorithm into the framework of the African vulture algorithm,the search range is expanded and the late-stage exploration ability is enhanced,thereby improving the global optimization capability of the algorithm.The effectiveness of the proposed algorithm in improving optimization accuracy is verified through mul-tiple test functions.The proposed algorithm is applied to the DMC control systems of desulfurization towers and distil-lation tower models.The control horizon and prediction horizon parameters are optimized through iteration,and set-point tracking control simulation and anti-interference tests are carried out.The simulation test results show that the optimized DMC controller can achieve fast and accurate set-point tracking,significantly reducing the overshoot and adjustment time.