Sm. Prabhu et Dp. Garg, FUZZY-LOGIC-BASED REINFORCEMENT LEARNING OF ADMITTANCE CONTROL FOR AUTOMATED ROBOTIC MANUFACTURING, Engineering applications of artificial intelligence, 11(1), 1998, pp. 7-23
An approach to admittance control using fuzzy-logic-based reinforcemen
t learning is proposed for the robotic automation of typical manufactu
ring operations. The proposed approach provides the necessary nonlinea
r control actions required in a typical automated robotic manufacturin
g task. Simultaneously, it reduces the controller development time due
to the incorporation of pre-existing process knowledge in a neural-ne
twork form. The pre-existing knowledge is further refined using reinfo
rcement learning via a CMAC (Cerebellar Model Articulation Controller)
based critic network. Automated robotic deburring offers an attractiv
e alternative to manual deburring in terms of reduced costs and improv
ed quality of the finished parts. Hence. robotic deburring is used as
an example of a typical manufacturing task to verify the performance o
f the proposed approach. However, the approach is general enough to be
easily extended to similar manufacturing tasks. Simulation results ar
e presented, which demonstrate the effectiveness of the proposed strat
egy in controlling the automated robotic deburring task. (C) 1998 Else
vier Science Ltd. All rights reserved.