Fast Data-Driven Predictive Control for LTI Systems: A Randomized Approach

Abstract

In this letter, the problem of reducing the computational complexity of a recently developed data-driven predictive control scheme is considered. For this purpose, a randomized data compression technique is proposed, which makes the dimension of the decision variable independent of the recorded data size, thereby reducing the complexity of the online optimization problems in data-driven predictive control to that of classical model-based predictive control. The proposed method outperforms other competing complexity reduction schemes in benchmark tests, while guaranteeing similar control performance and stability properties.