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This study proposes data‐driven model‐free adaptive control (MFAC), model‐free control (MFC) and virtual reference feedback tuning (VRFT) techniques applied to the control of a representative non‐linear multi‐input–multi‐output (MIMO) system represented by the twin rotor aerodynamic system (TRAS). These data‐driven techniques are implemented for both a single MIMO controller and two separately designed single‐input–single‐output controllers running in parallel for azimuth and pitch control. The three techniques are implemented as MFAC and MFC algorithms and as linear controllers tuned by VRFT. The performance of the three data‐driven MIMO control system structures is compared systematically on the basis of the experimental results in terms of the values of the sum of mean squared control errors measured on TRAS equipment. [ABSTRACT FROM AUTHOR]

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]