NDLI: A neurobotics model of a multi-joint arm movements control

Content Provider	IEEE Xplore Digital Library
Author	Khemaissia, S.
Copyright Year	2004
Description	Author affiliation: Riyadh Coll. of Technol., Saudi Arabia (Khemaissia, S.)
Abstract	One of the amazing successes of biological systems is the animal's ability to learn to control the complicated dynamics of their muscles and joints smoothly and efficiently. Traditional engineering control techniques, on the other hand, often do not perform well when confronted with intrinsically complex systems with many degrees of freedom, such as robot arm (human arm). In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematics features of movement. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centres, which represent the motor goal in the Cartesian space. The controller computes joint trajectories and excitation signals according to a minimum jerk criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. This intelligent neuro-adaptive model is used as a hybrid force/position controller for a dual arm. To optimise the neural network learning strategy, a hybrid neuro-genetic algorithm is introduced and simulation results are given for comparisons.
Starting Page	1426
Ending Page	1431
File Size	966801
Page Count	6
File Format	PDF
ISBN	0780386620
DOI	10.1109/ICIT.2004.1490772
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-08
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Biological system modeling Biological control systems Control systems Biological neural networks Force control Biological systems Robot kinematics Humans Motor drives
Content Type	Text
Resource Type	Article

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