Salazar, Juan Augusto ParedesGoel, Ankit2024-11-142024-11-142024-10-09https://doi.org/10.48550/arXiv.2410.06556http://hdl.handle.net/11603/36956Model 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.8 pagesen-USAttribution 4.0 International CC BY 4.0 Deedhttps://creativecommons.org/licenses/by/4.0/Computer Science - Systems and ControlUMBC Estimation, Control, and Learning Laboratory (ECLL).Electrical Engineering and Systems Science - Systems and ControlText