Nonlinear Predictive Cost Adaptive Control of Pseudo-Linear Input-Output Models Using Polynomial, Fourier, and Cubic Spline Observables

Abstract

Control of nonlinear (NL) systems with high levels of uncertainty is practically relevant and theoretically challenging. This paper presents a numerical investigation of an adaptive NL model predictive control (MPC) technique that relies entirely on online system identification without prior modeling, training, or data collection. In particular, the paper extends predictive cost adaptive control (PCAC) for linear systems, which is an extension of generalized predictive control, to NL systems. NL PCAC (NPCAC) uses recursive least squares (RLS) with subspace of information forgetting (SIFt) to identify a discrete-time, pseudo-linear, input-output model, which is used with iterative MPC for NL receding-horizon optimization. The performance of NPCAC is illustrated using polynomial, Fourier, and cubic-spline basis functions.

Accepted and presented at IEEE CCTA 2026