Adaptive model-free disturbance rejection for continuum robots

• Published in: Automatica (Oxford) Vol. 171; p. 111949
• Main Authors: Yilmaz, Cemal Tugrul, Watson, Connor, Morimoto, Tania K., Krstic, Miroslav
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Continuum robots; Disturbance rejection; Model-free control; Neural-networks; Prescribed-time control; Prescribed-time control; Disturbance rejection; Continuum robots; Neural-networks; Model-free control
• ISSN: 0005-1098
• DOI: 10.1016/j.automatica.2024.111949

This paper presents two model-free control strategies for the rejection of unknown disturbances in continuum robots. The strategies utilize a neural network-based approximation technique to estimate the uncertain Jacobian matrix using position measurements. The first strategy is designed for periodic disturbances and employs an adaptive model-free controller in conjunction with an adaptive disturbance observer. The second strategy is designed for robustness against arbitrary disturbances and employs time-varying input and update law gains that grow monotonically, resulting in the achievement of asymptotic, exponential, and prescribed-time reference trajectory tracking. The notion of fixed-time stabilization in prescribed time is particularly noteworthy, as it allows for the predefinition of a terminal time, independent of initial conditions and system parameters. A formal stability analysis is presented for each strategy, and the strategies are both tested experimentally with a concentric tube robot subject to unknown disturbances.