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

This paper proposes a data-based model-free approach to reference trajectory tracking in two-degree-offreedom (2-DOF) nonlinear control system 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 nonlinear 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 nonlinear aerodynamic twin-rotor system shows that our approach can effectively improve the control system performance.