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Published in:
2013 | OriginalPaper | Chapter
Recursive Model Predictive Control for Fast Varying Dynamic Systems
Authors : Da Lu, Guangzhou Zhao, Donglian Qi
Published in: Intelligent Computing for Sustainable Energy and Environment
Publisher: Springer Berlin Heidelberg
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A well known drawback of model predictive control (MPC) is that it can only be adopted in slow dynamics, where the sample time is measured in seconds or minutes. The main reason leads to the problem is that the optimization problem included in MPC has to be computed online, and its iterative computational procedure requires long computational time. To shorten computational time, a recursive approach based on Iterative Learning Control (ILC) and Recursive Levenberg Marquardt Algorithm (RLMA) is proposed to solve the optimization problem in MPC. Then, recursive model predictive control (RMPC) is proposed to realize MPC for fast varying dynamic systems. Simulation results show the effectiveness of RMPC compared with conventional MPC.