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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:2609.09380v1 Announce Type: cross Abstract: Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and…
arXiv:2609.09236v1 Announce Type: new Abstract: A latent space network model places the nodes in a metric space and lets the probability of a tie decrease with distance. In a space of constant curvature, pairwise distances determine the positions up to an isometry.
arXiv:2609.07433v1 Announce Type: new Abstract: This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints.
