In this work, a decentralized control strategy for tightly connected networked Lagrangian systems is designed. The main characteristics of the solution is that it allows to both control the motion of the handled object and the squeezing wrenches arising on it and this is achieved by resorting to a layered architecture. At the top layer, robots exploit consensus theory to distributedly estimate the full state of the system and the object dynamics to estimate the squeezing wrenches, while, at the second layer, a local adaptive control law is specified in order to both control the local contribute to the squeezing wrenches and the local motion of the robot. The effectiveness of the solution is proven by employing 6-DOFs serial chain manipulators mounted on a mobile platform to perform a cooperative load transportation task.

A Decentralized Robust Adaptive Control for Tightly Connected Networked Lagrangian Systems

marino alessandro
2017

Abstract

In this work, a decentralized control strategy for tightly connected networked Lagrangian systems is designed. The main characteristics of the solution is that it allows to both control the motion of the handled object and the squeezing wrenches arising on it and this is achieved by resorting to a layered architecture. At the top layer, robots exploit consensus theory to distributedly estimate the full state of the system and the object dynamics to estimate the squeezing wrenches, while, at the second layer, a local adaptive control law is specified in order to both control the local contribute to the squeezing wrenches and the local motion of the robot. The effectiveness of the solution is proven by employing 6-DOFs serial chain manipulators mounted on a mobile platform to perform a cooperative load transportation task.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4702827
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