Multi-Model Predictive Control with Kalman Filter on DC/DC Converter

Abstract:

In this paper, a new kind of Multi-model Predictive Controller (MMPC) is presented. Multiple models switch MMPC with Kalman filter based on neural network optimization was implemented in a DC/DC converter. Multiple models direct switch was used according to the two modes of the Boost-Buck circuit and different loads condition. Kalman filter was successfully added to the MMPC to transfer the optimal state value to MMPC from the output of the DC/DC circuit. Parameters optimization method is also discussed in the paper when the controller was applied to real model simulation. Simulation result demonstrates that the good performance of MMPC compared with single model predictive control and PID control. With the help of high speed FPGA, it is possible to apply this method to hardware simulation.

• Periodical: Applied Mechanics and Materials (Volumes 401-403)
• Pages: 1614-1618
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.401-403.1614
• Online since: September 2013
• Authors: Chen Zeng, Li Yan Zhang, Ling Hui Zeng
• Keywords: Dc-Dc Converter, Kalman Filter (KF), Multi-Model Predictive Controller, Simulation

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