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arXiv · 2410.06556

MPC-guided, Data-driven Fuzzy Controller Synthesis

Juan Augusto Paredes Salazar·Ankit Goel
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Abstract

Model predictive control (MPC) is a powerful control technique for online optimization using system model-based predictions over a finite time horizon. However, the computational cost MPC requires can be prohibitive in resource-constrained computer systems. This paper presents a fuzzy controller synthesis framework guided by MPC. In the proposed framework, training data is obtained from MPC closed-loop simulations and is used to optimize a low computational complexity controller to emulate the response of MPC. In particular, autoregressive moving average (ARMA) controllers are trained using data obtained from MPC closed-loop simulations, such that each ARMA controller emulates the response of the MPC controller under particular desired conditions. Using a Takagi-Sugeno (T-S) fuzzy system, the responses of all the trained ARMA controllers are then weighted depending on the measured system conditions, resulting in the Fuzzy-Autoregressive Moving Average (F-ARMA) controller. The effectiveness of the trained F-ARMA controllers is illustrated via numerical examples.

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Juan Augusto Paredes Salazar, Ankit Goel. 2024-10-09. MPC-guided, Data-driven Fuzzy Controller Synthesis. https://arxiv.org/abs/2410.06556

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