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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:2610.20041v1 Announce Type: cross Abstract: A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data.
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arXiv:2610.18400v1 Announce Type: new Abstract: Data trading markets that span industries and regions are expanding, but the implicit trust that supports data use within an organization does not carry over to cross-domain exchange.
