Accurate modelling of nonlinear dynamical systems remains a fundamental challenge in control engineering, particularly in applications characterized by strong nonlinearities, uncertainty, and varying operating conditions such as anti-lock braking systems (ABSs). Although neural networks are widely used for nonlinear system identification, their performance is often limited by correlated input features, poor numerical conditioning, and reliance on computationally demanding nonlinear optimization. This paper proposes a novel neural network modelling framework that integrates improved almost-orthogonal functional input transformation with a linear-in-parameters structure. The proposed approach systematically constructs a nonlinear feature space in which correlations between basis functions are explicitly controlled through a perturbation-based near-orthogonality mechanism, resulting in improved conditioning of the regression matrix and enabling stable least-squares-based parameter estimation. The method is formulated for a general class of nonlinear discrete-time systems and experimentally validated on an Inteco ABS laboratory setup, where wheel slip dynamics are identified using measured wheel speeds and braking torque. The obtained results demonstrate improved modelling accuracy, increased robustness to measurement noise, non-Gaussian disturbances, and parameter drift, as well as lower computational complexity compared with conventional multilayer perceptron and polynomial-based models. These findings suggest that structured feature generation may improve the reliability of data-driven models and indicate potential applicability of the proposed framework for real-time and control-oriented applications in complex dynamical systems.
Perić et al. (2026) studied this question.