{'fraction_non_alphanumeric': 0.010494752623688156, 'fraction_numerical': 0.0, 'mean_word_length': 5.6072607260726075, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Tracking control of tomographic image acquisition robotic system based on active disturbance rejection theory with adaptive gains

The aim of this study was to design an output-based automatic controller that solves the trajectory tracking of a tomographic image acquisition robotic system. The technique used to design the controller was the active disturbance rejection control. The design process included the adaptation of controller gains depending on the tracking error. The application of this controller induced a smoother approaching of the tomographic image acquisition robotic system states to the reference trajectories. The output-based controller design offered a better approximation of the non-modeled sections in tomographic image acquisition robotic system yielding a better tracking of the reference states needed in the tomographic acquisition of images with potential application in medical diagnosis or treatment. The introduction of adaptive gains enforced a virtual increment in the number of states included in the extended state observer. The controller used the estimated velocity states calculated by an extended state adaptive observer. A set of numerical evaluations executed over a simulated version of the tomographic image acquisition robotic system proved the efficiency of the adaptive tracking trajectory controller. The quality of control execution was evaluated by determining the root mean square index of the tracking error, and the controller was also evaluated on an experimental platform. This part of the study compared the results of implementing three controllers: a basic proportional–derivative controller, a generalized proportional integral controller based on a constant gain observer, and the controller proposed in this study with time-varying gains. This comparison showed an equally faster convergence for all the three control evaluations but with smaller oscillations in the case of the adaptive version as well as a smaller steady-state error.