Position estimation and object collision detection of a tendon-driven actuator based on a polytopic observer synthesis

Benoit Huard (1, 2) , Mathieu Grossard (1) , Sandrine Moreau (2, 3) , Thierry Poinot (3, 2)

1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs
2 LIAS - Laboratoire d'Informatique et d'Automatique pour les Systèmes
3 LAII - Laboratoire d'Automatique et d'Informatique Industrielle

This paper proposes a multi-model based approach that estimates joint position and collision detection of a lightweight electric actuator dedicated to manipulation tasks. Taking advantage of an optimized back-drivable mechatronic design, the use of proprioceptive measures at the motor level enables the estimation of the unmeasured terminal position of the mechanical motor-to-joint transmission and force contact disturbance. A polytopic formulation is introduced based on an accurate model of the mechatronic transmission. Then, a multi-model observer-based estimator is synthesized using root-clustering for exteroceptive variables estimation without additional position/force sensor. The approach is experimentally validated with a single degree-of-freedom manipulator.

Keywords: Back-drivable actuator; Least-squares identification; Polytopic convex transformation; Multi-model observer; Linear matrix inequalities; Collision detection

Domains: Engineering Sciences [physics]

Benoit Huard, Mathieu Grossard, Sandrine Moreau, Thierry Poinot. Position estimation and object collision detection of a tendon-driven actuator based on a polytopic observer synthesis. Control Engineering Practice, 2013, 21, pp.1178 - 1187. ⟨10.1016/j.conengprac.2013.04.008⟩. ⟨cea-01778155⟩