Abstract
This paper presents neuro-fuzzy control approach for MIMO systems. Motivated by the hybrid force/position control of robot manipulator problem, a systematic design procedure is proposed for fuzzy rules generation and optimization. The construction of the proposed neuro-fuzzy controller consists in three phases. In the first phase, which is called parameters learning phase, the neuro-fuzzy system is considered as feed-forward neural network and back-propagation learning algorithm is then applied for parameters identification in order to map Input/Output data. In the second phase, a new clustering algorithm based on the inclusion concept is used for optimal clusters identification. Finally, the fuzzy rules base is generated and optimized. The performances of the control approach are improved by introducing a Reference Model (RM), which is able to cope with Robot/Environment interaction variations. A 2 DOF planar manipulator force/position control simulation is presented and results discussed.
Published Version
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