Data-driven iterative learning control for nonlinear multivariate systems using transpose adaptive filtering
Journal
European Journal of Control
Journal Volume
85
Start Page
101273
ISSN
0947-3580
Date Issued
2025-09
Author(s)
Abstract
This paper presents a novel data-driven iterative learning control (ILC) algorithm for stabilized multivariable nonlinear dynamical systems. The proposed algorithm incorporates two data-driven learning mechanisms: an adaptive feedforward algorithm that models perturbed dynamics as an unknown linear time-varying system and minimizes RMS errors with respect to an LTI reference model, followed by a second learning mechanism for fast convergence in trajectory tracking. For multivariate systems, the non-commutative nature of the cascade of systems necessitates the use of the right inverse for adaptive model matching to promote error convergence. To address challenges in adaptive filtering, a transposition-based technique is introduced to obtain the right inverse for square and over-actuated systems. For ni-input-no-output systems, the approach necessitates conducting ni×no experiments. An efficient algorithm is proposed to reduce this requirement by reorganizing impulse response matrix components. The effectiveness of the proposed methods is demonstrated through both simulations and experiments.
Subjects
Adaptive model matching
Dynamic coupling
Iterative learning control
Multivariate inversion
Publisher
Elsevier BV
Type
journal article