Data-based two-degree-of-freedom iterative control approach to constrained non-linear systems

Abstract

This study proposes a data-based model-free approach to reference trajectory tracking in two-degree-of-freedom non-linear control system (CS) structures. This model-free control approach tunes both the feedback controller parameters and the reference input sequence accounting for control saturation and control rate constraints. The controller is iteratively tuned in a non-linear framework that employs a gradient descent search approach. The model-free gradient estimates are obtained by a perturbation-based approach. The reference input tuning is carried out in a linear framework using an iterative learning control-based approach, and it also includes a model-free gradient search algorithm where the gradient estimates are obtained by a similar perturbation-based approach. The number of real-world experiments is significantly reduced by the use of simulated models identified as neural networks. A digitally simulated case study concerning the angular position control of a non-linear aerodynamic twin-rotor system shows that the author's approach can effectively improve the CS performance.