Paper Info
Title
Deep Reinforcement Learning Based Self-Configuring Integral Sliding Mode Control Scheme For Robot Manipulators
Abstract
This paper deals with the design of an intelligent self-configuring control scheme for robot manipulators. The scheme features two control structures: one of centralized type, implementing the inverse dynamics approach, the other of decentralized type. In both control structures, the controller is based on Integral Sliding Mode (ISM), so that matched disturbances and uncertain terms, due to unmodeled dynamics or couplings effects, are suitably compensated. The use of the ISM control also enables the exploitation of its capability of acting as a "perturbation estimator" which, in the considered case, allows us to design a Deep Reinforcement Learning (DRL) based decision making mechanism. It implements a switching rule, based on an appropriate reward function, in order to choose one of the two control structures present in the scheme, depending on the requested robot performances. The proposed scheme can accommodate a variety of velocity and acceleration requirements, in contrast with the genuine decentralized or centralized control structures taken individually. The assessment of our proposal has been carried out relying on a model of the industrial robot manipulator COMAU SMART3-S2, identified on the basis of real data and with realistic sensor noise.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619843
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Integral sliding mode,Control theory,Control theory,Computer science,Industrial robot,Acceleration,Inverse dynamics,Robot,Estimator,Reinforcement learning
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Bianca Sangiovanni111.05
Gian Paolo Incremona2709.40
A. Ferrara3953126.03
Marco Piastra411.73