Active Disturbance Rejection Control Tuned by Fictitious Reference Iterative Tuning for Tower Crane Systems

The new aspects provided by the current paper consist of merging the features of the two data-driven algorithms, Active Disturbance Rejection Control (ADRC) and Fictitious Reference Iterative Tuning (FRIT), that are reunited as ADRC-FRIT algorithm to improve the control performances of the three degrees of freedom Tower Crane Systems (TCSs). The ADRC-FRIT algorithm determines the optimal controller parameters by solving a gradient problem using the classical Gauss-Newton algorithm. Both ADRC and FRIT algorithms belong to the category of data-driven algorithms, i.e., the input/output data are used to compute the controller parameters. A comparison study of the new ADRC-FRIT algorithm with the ADRC algorithm is performed, where both algorithms are proven through experimental evaluation using the TCS laboratory equipment.