Learning-Based Efficient Approximation of Data-Enabled Predictive Control

TL;DR

This paper introduces a data-driven control approximation method that significantly reduces computational complexity while maintaining performance, enabling efficient control of resource-limited systems.

Contribution

It proposes a size-invariant approximation of DeePC using differentiable convex programming, improving computational efficiency without sacrificing control quality.

Findings

Reduces DeePC computational time by a factor of 5

Maintains control performance with the approximation

Validated on a quadruple tank system

Abstract

Data-Enabled Predictive Control (DeePC) bypasses the need for system identification by directly leveraging raw data to formulate optimal control policies. However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application to resource-constrained systems due to high computational costs. In this paper, we propose an efficient approximation of DeePC, whose size is invariant with respect to the amount of data collected, via differentiable convex programming. Specifically, the optimization problem in DeePC is decomposed into two parts: a control objective and a scoring function that evaluates the likelihood of a guessed I/O sequence, the latter of which is approximated with a size-invariant learned optimization problem. The proposed method is validated through numerical simulations on a quadruple tank system, illustrating that the learned controller can reduce the computational time of DeePC by a factor of 5 while maintaining its control performance.