Neural Network Iterative Learning for SISO Non-Affine Control Systems

Abstract

This work introduces an identification scheme capable of obtaining the unknown dynamics of a nonlinear plant process. The proposed method employs an iterative algorithm that prevents confinement to a sole trajectory by fitting a neural network over a series of trajectories that span the desired subset of the state space. At the core of our contributions lie the applicability of our method to open-loop unstable systems and a novel way of generating the system’s reference trajectories, which aim at sufficiently stimulating the underlying dynamics. Following this, the prescribed performance control (PPC) technique is utilized to ensure accurate tracking of the aforementioned trajectories. The effectiveness of our approach is showcased through successful identification of the dynamics of a two-degree of freedom (DOF) robotic manipulator in both a simulation study and a real-life experiment.