# MPC-guided, Data-driven Fuzzy Controller Synthesis

arXiv: https://arxiv.org/abs/2410.06556 · HTML: https://mlsanity.com/paper/2410.06556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following](https://mlsanity.com/paper/2504.06500/) (2025)

## Abstract (arXiv)

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.