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

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• 作者：Benoit Huard ; Mathieu Grossard ; S ; rine Moreau ; Thierry Poinot
• 关键词：Back-drivable actuator ; Least-squares identification ; Polytopic convex transformation ; Multi-model observer ; Linear matrix inequalities ; Collision detection
• 刊名：Control Engineering Practice
• 出版年：2013
• 出版时间：September, 2013
• 年：2013
• 卷：21
• 期：9
• 页码：1178-1187
• 全文大小：3781 K

文摘

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.