Adaptive Behavioral Predictive Control: State-Free Regulation Without Hankel Weights

Tam W. Nguyen

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arXiv · 2602.12016

Adaptive Behavioral Predictive Control: State-Free Regulation Without Hankel Weights

Tam W. Nguyen

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Abstract

This paper presents adaptive behavioral predictive control (ABPC), an indirect adaptive predictive control framework operating on streaming data. An LPV--ARX predictor is identified online via kernel--recursive least squares and used to compute closed-form predictive control sequences over a finite horizon, avoiding batch Hankel constructions and iterative optimization. Nonlinear kernel dictionaries extend model expressiveness within a behavioral formulation. Numerical studies on Hammerstein and NARX systems demonstrate effective performance when the dictionary aligns with the plant class and highlight conditioning and feature-selection effects. The paper emphasizes numerical simulation, computational feasibility, and reproducibility.

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Tam W. Nguyen. 2026-02-12. Adaptive Behavioral Predictive Control: State-Free Regulation Without Hankel Weights. https://arxiv.org/abs/2602.12016

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