Paper Info
Title
Learning for the Control of Dynamical Motion Systems
Abstract
This paper addresses the dynamic control of multi- joint systems based on learning of sensory-motor transformations. To avoid the dependency of the controllers to the analytical knowledge of the multi- joint system, a non parametric learning approach is developed which identifies non linear mappings between sensory signals and motor commands involved in control motor systems. The learning phase is handled through a General Regression Neural Network (GRNN) that implements a non parametric Nadarayan-Watson regression scheme and a set of local PIDs. The resulting dynamic sensory-motor controller (DSMC) is intensively tested within the scope of hand-arm reaching and tracking movements in a dynamical simulation environment. (DSMC) proves to be very effective and robust. Moreover, it reproduces kinematics behaviors close to captured hand-arm movements.
Year
DOI
Venue
2007
10.1109/ISDA.2007.27
ISDA
Keywords
Field
DocType
dynamic sensory-motor controller,joint system,dynamic control,sensory-motor transformation,control motor system,general regression neural network,dynamical motion systems,hand-arm movement,non parametric,non linear mapping,analytical knowledge,dynamic simulation,motion control,motor system
Dynamical simulation,Motion control,Control theory,Kinematics,Nonlinear system,Regression,Computer science,Control theory,Nonparametric statistics,Motor system
Conference
ISSN
ISBN
Citations 
2164-7143
0-7695-2976-3
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Pierre-Francois Marteau18214.62
Sylvie Gibet236752.50