Abstract
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.
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Lu, D., Zhao, G., Qi, D. (2013). Recursive Model Predictive Control for Fast Varying Dynamic Systems. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_12
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DOI: https://doi.org/10.1007/978-3-642-37105-9_12
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