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A nonlinear adaptive predictive control method (NPCAC) is proposed that identifies a pseudo-linear model online from input-output data without prior training. It combines recursive least squares with information forgetting and iterative MPC, tested with polynomial, Fourier, and spline basis functions. The approach handles systems with high uncertainty.
Why it matters
This is relevant for real-time control of complex nonlinear processes requiring rapid adaptation.
Original headline
Nonlinear Predictive Cost Adaptive Control of Pseudo-Linear Input-Output Models Using Polynomial, Fourier, and Cubic Spline Observables
arXiv:2501.05815v2 Announce Type: replace Abstract: This paper introduces a novel nonlinear model predictive control (NMPC) framework that incorporates a lifting technique to enhance control performance for nonlinear systems.
arXiv:2607.19639v2 Announce Type: replace-cross Abstract: Motivated by stochastic model predictive control applications, we develop a semialgebraic framework for constraint tightening in chance-constrained systems with unbounded additive disturbances and saturated inputs.
arXiv:2609.30269v1 Announce Type: new Abstract: Why does a superior technology sometimes spread and sometimes stall, even when its expected returns are much higher? We develop an agent-based model in which firms adopt a digital technology by imitating successful neighbours through a fast-and-frugal…
