Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in “trajectory space.” Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This data-driven optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and compare norm-based and manifold-embedding-based data selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators—rocket-landing, a robotic arm, and cart-pole inverted pendulum swing-up—comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.