TY - Generic T1 - Towards Minimum-Information Adaptive Controllers for Robot Manipulators T2 - American Control Conference, AMC2017 Y1 - 2017 A1 - T. Marcucci A1 - C. Della Santina A1 - M Gabiccini A1 - A. Bicchi KW - Robotics AB -

The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between
which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters.
Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot.

JF - American Control Conference, AMC2017 PB - IEEE CY - May 24–26 2017, Seattle USA UR - https://ieeexplore.ieee.org/document/7963602 ER -