Data-Enabled Predictive Control (DeePC)
Using raw data directly for predictive control of complex systems, bypassing the need for explicit parametric models.
Data-Enabled Predictive Control (DeePC) is a control framework that uses measured input-output data to directly predict and optimize future system behavior, without first identifying a parametric model. DeePC replaces the traditional model identification step with a data-driven representation of the system’s behavior, enabling real-time optimal control directly from data.
Our work on DeePC spans the full spectrum from theory to practice. On the theoretical side, we have developed regularization techniques that make DeePC robust to noise and model mismatch, drawing connections between regularized DeePC and classical system identification approaches. On the practical side, we have demonstrated DeePC on quadrotors and autonomous excavators, showing that the approach can handle the complexities present in real-world robotic systems.