Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems

This article presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data. The algorithm combines robust predictive control and a multistep predictor of nonlinear systems obtained from regularized kernel methods. The latter is based on implicitly learning the nonlinear behavior of the system via the representer theorem. Instead of seeking a model and then performing control design, our method goes directly from data to control. This allows us to robustify the control inputs against the uncertainties in data by considering a min-max optimization problem to calculate the robust and optimal control sequence. We show that by incorporating an appropriate uncertainty set, this min-max problem can be reformulated as a nonconvex but structured minimization problem. By exploiting its structure, we present a projected gradient descent algorithm to effectively solve this problem. Finally, we test the RoKDeePC method on two nonlinear example systems—one academic case study and a grid-forming converter feeding a nonlinear load—and compare it with some existing nonlinear data-driven predictive control methods.