# Synthesis of model predictive control based on data-driven learning

## TL;DR

This paper introduces a data-driven model predictive control method that iteratively learns system dynamics without prior models, enabling adaptive control of complex, time-varying, or nonlinear systems.

## Contribution

It presents a novel MPC approach that uses data-driven learning to approximate system parameters and adapt to changing dynamics without requiring system matrices beforehand.

## Key findings

- Successfully predicts and optimizes future system behaviors.
- Adapts to time-varying and nonlinear plant dynamics.
- Provides a data-driven identification tool for complex systems.

## Abstract

For the application of MPC design in on-line regulation or tracking control problems, several studies have attempted to develop an accurate model, and realize adequate uncertainty description of linear or non-linear plants of the processes. In this study, we employ the data-driven learning technique to iteratively approximate the dynamical parameters, without requiring a priori knowledge of system matrices. The proposed MPC approach can predict and optimize the future behaviors using multiorder derivatives of control input as decision variables. Because the proposed algorithm can obtain a linear system model at each sampling, it can adapt to the actual dynamics of time-varying or nonlinear plants. This methodology can serve as a data-driven identification tool to study adaptive optimal control problems for unknown complex systems.