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Data-Driven Predictive Control for Nonlinear Systems: A Structured Prediction Approach With Kernelized Innovation Feedback
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DOI：10.1002/rnc.70655.png)
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Abstract
En 中文
Data-driven predictive control (DDPC) methods have received increasing interest and gained exceptional success in linear systems. However, extending DDPC to nonlinear systems remains a challenging task, primarily due to the difficulty in balancing the complexity of the output predictor (OP) with generalization capability, and the inability of a static OP to cope with real-time unmodeled dynamics. In this paper, we propose a novel nonlinear DDPC framework via a structured OP and a kernelized innovation-based feedback mechanism. To effectively capture nonlinear dynamics from data, we first design a new structured predictor that consists of a nominal linear term for capturing coarse-grained linear relations and a nonlinear term established in the reproducing kernel Hilbert space (RKHS), capturing fine-grained nonlinearity. To further enhance robustness against unmodeled dynamics and disturbance, a kernelized feedback mechanism is designed to correct predicted outputs based on real-time innovation sequences. A tailored heuristic algorithm based on alternating minimization is designed to effectively solve the data-driven parameter estimation problem. By applying the data-driven OP in the control regime, a new DDPC method for nonlinear systems is derived. Comprehensive studies on a nonlinear numerical example and a robotic manipulator demonstrate that the proposed method yields lower prediction errors and better tracking performance than the existing linear and nonlinear DDPC methods.
Keywords:
data-driven predictive control
innovation-based feedback
nonlinear systems
structured predictor
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