Data-Driven Predictive Control for Continuous-Time Industrial Processes with Completely Unknown Dynamics

TL;DR

This paper introduces a data-driven predictive control method for continuous-time industrial processes with unknown dynamics, using online system identification and model-free control to achieve stable reference tracking.

Contribution

It presents a novel approach combining online system matrix estimation with model-free predictive control for unknown continuous-time systems.

Findings

Algorithm is feasible and stable.

Effective in reference tracking.

Validated through simulation example.

Abstract

This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach employs the data-driven technique to get the system matrices online, using input-output measurements. Then, a model-free predictive control approach is designed to implement the receding-horizon optimization and realize the reference tracking. Feasibility of the proposed algorithm and stability of the closed-loop control systems are analyzed, respectively. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach.