Online Reduced-Order Data-Enabled Predictive Control

TL;DR

This paper introduces an online reduced-order data-enabled predictive control framework that adaptively updates system models with real-time data, improving control of time-varying systems without relying on extensive pre-collected data.

Contribution

It proposes a novel online DeePC method that updates Hankel matrices in real-time for systems with evolving dynamics, using singular value insights and reduced-order modeling.

Findings

Effective control of linear time-varying systems demonstrated

Improved anti-rollover vehicle control shown in simulations

Reduced computational complexity achieved with SVD technique

Abstract

Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit system models. Traditional DeePC methods use pre-collected input/output (I/O) data to construct Hankel matrices for online predictive control. However, in systems with evolving dynamics or insufficient pre-collected data, incorporating real-time data into the DeePC framework becomes crucial to enhance control performance. This paper proposes an online DeePC framework for time-varying systems (i.e., systems with evolving dynamics), enabling the algorithm to update the Hankel matrix online by adding real-time informative signals. By exploiting the minimum non-zero singular value of the Hankel matrix, the developed online DeePC selectively integrates informative data and effectively captures evolving system dynamics. Additionally, a numerical singular value decomposition technique is introduced to reduce the computational complexity for updating a reduced-order Hankel matrix. Simulation results on two cases, a linear time-varying system and the vehicle anti-rollover control, demonstrate the effectiveness of the proposed online reduced-order DeePC framework.