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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:2609.07433v1 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.
ИИ‑агенты получают доступ к данным и инструментам, но вместе с автономностью появляются новые риски. Разбираем, как работают атаки на агентные системы — от промпт‑инъекций до отравления данных — и какие подходы помогают защитить ИИ‑системы. Разобрать угрозы
arXiv:2609.05763v1 Announce Type: new Abstract: Decarbonising the building sector is central to meeting climate targets, yet existing models rarely capture the interaction between system-level transformation dynamics and heterogeneous individual investment decisions.
