Comparative Analysis of Data-Driven Predictive Control Strategies

TL;DR

This paper systematically compares leading data-driven predictive control strategies, analyzing their theoretical bases, assumptions, and performance through a numerical benchmark.

Contribution

It provides a comprehensive review and comparison of three major data-driven predictive control methods, highlighting their theoretical differences and practical implications.

Findings

The strategies have distinct theoretical foundations and assumptions.

Performance varies significantly depending on the control scenario.

The numerical benchmark offers a rigorous basis for comparison.

Abstract

This paper compares data-driven predictive control strategies by examining their theoretical foundations, assumptions, and applications. The three most widely recognized and consequential methods, Data Enabled Predictive Control, Willems-Koopman Predictive Control, Model-Free Adaptive Predictive Control are employed. Each of these strategies is systematically reviewed, and the primary theories supporting it are outlined. Following analysis, a discussion is provided regarding their fundamental assumptions, emphasizing their influence on control effectiveness. A numerical example is presented as a benchmark for comparison to enable a rigorous performance evaluation.