MPC-guided, Data-driven Fuzzy Controller Synthesis
<|reference_start|>MPC-guided, Data-driven Fuzzy Controller Synthesis: 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.<|reference_end|>
@article{salazar2024mpc-guided,,
    title={MPC-guided, Data-driven Fuzzy Controller Synthesis},
    author={Juan Augusto Paredes Salazar, Ankit Goel},
    journal={arXiv preprint arXiv:2410.06556},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06556},
    primaryClass={eess.SY cs.SY}
}