Learning for the Control of Dynamical Motion Systems
Résumé
This paper addresses the dynamic control of multijoint systems based on learning of sensory-motor transformations. To avoid the dependency of the controllers to the analytical knowledge of the multijoint 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.
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