2023/08/27 by Baiwei Guo, Guo, Baiwei, Yuning Jiang +5
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #Fault Detection and Control Systems
paper · pdf · doi:10.48550/arxiv.2308.14178
For linear systems, many data-driven control methods rely on the behavioral framework, using historical data of the system to predict the future trajectories. However, measurement noise introduces errors in predictions. When the noise is bounded, we propose a method for designing historical experiments that enable the computation of an upper bound on the prediction error. This approach allows us to formulate a minimax control problem where robust constraint satisfaction is enforced. We derive an upper bound on the suboptimality gap of the resulting control input sequence compared to optimal control utilizing accurate measurements. As demonstrated in numerical experiments, the solution derived by our method can achieve constraint satisfaction and a small suboptimality gap despite the measurement noise.