DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization

TL;DR

DeePC-Hunt is a novel differentiable optimization method that automatically tunes hyperparameters for the DeePC algorithm, improving closed-loop control performance and robustness in complex nonlinear systems.

Contribution

It introduces a backpropagation-based hyperparameter tuning approach for DeePC, enabling direct optimization of closed-loop performance.

Findings

DeePC-Hunt effectively improves stabilization in nonlinear systems.

The method outperforms traditional model-based control in robustness.

Numerical simulations validate the approach's effectiveness.

Abstract

This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing the open-loop performance can result in unacceptable closed-loop behavior, while manual guess-and-check can pose safety challenges. DeePC-Hunt provides an alternative method for hyperparameter tuning which uses an approximate model of the system dynamics and backpropagation to directly optimize hyperparameters for the closed-loop DeePC performance. Numerical simulations demonstrate the effectiveness of DeePC in combination with DeePC-Hunt in a complex stabilization task for a nonlinear system and its superiority over model-based control strategies in terms of robustness to model misspecifications.