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

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. [ABSTRACT FROM AUTHOR]