Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 15 references
Abstract
This paper presents a Kernelized Data-Driven Predictive Control (KDPC) scheme for robust, offset-free tracking of nonlinear systems. To overcome the computational burden of direct data-driven methods, we employ a hybrid framework that learns the nonlinear dynamics in a Reproducing Kernel Hilbert Space (RKHS) via joint ridge regression. A key contribution is the derivation of an analytical linearization of the kernel map, which renders the control problem a strictly convex Quadratic Program (QP) for efficient real-time implementation. We provide rigorous guarantees for recursive feasibility using terminal ingredients and establish Input-to-State Stability (ISS) with respect to the kernel approximation error. Finally, a simulation study on a Van der Pol oscillator is provided to illustrate the disturbance rejection and offset-free tracking capabilities of the proposed KDPC.
A theoretical analysis of the closed-loop properties of a data-driven kernel-based predictive control (DDKPC) scheme developed solely from input-output data proves that the DDKPC scheme guarantees recursive feasibility and closed-loop stability.
Wenjie Liu, Yifei Li, Gang Wang et al.· 0 citations
This work proposes a data-driven predictive control framework for nonlinear systems that incorporates data column preferences according to their proximity to the current operating point through a weighted norm regularization, thereby localizing the predictor without discarding any data.
F. Engeln, S. Zieglmeier, Marta A. Zagorowska et al.· 0 citations
A novel nonlinear DDPC framework via a structured OP and a kernelized innovation‐based feedback mechanism is proposed, which yields lower prediction errors and better tracking performance than the existing linear and nonlinear DDPC methods.
Yibo Wang, Yunxiang Ma, Tao Liu et al.· International Journal of Rob...· 0 citations
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, we derive a multi-step predictor that separates the effects of past data, future inputs, scheduling trajectories, and innovations. By project...
Federico Porcari, C. Verhoek, V. Breschi et al.· arXiv.org· 0 citations
