Abstract:
This paper presents contact force control of a one link flexible arm consisting of a simple boundary feedback of
bending moment at the base of the flexible arm. Gain tuning control system using neural network was developed and its control
performance examined and compared with fixed gains by numerical simulation and experiment. In this work, feedback gain
was tuned to correspond to the coupling coefficient of the neural network, and stabilized the learning by giving the initial value
to the coupling coefficient of the neural network, thereby shortening the learning time. To adjust the gain value in real time,
a sequential correction type technique(online learning) that repeats learning at every sampling was adopted as the learning
scheme of the neural network. As a result, it was confirmed that by using the neural network, the value of the feedback
gain was adaptively changed and the target contact force converged after 0.35 seconds. Comparing the performance with that
obtained with fixed gain, it was found that neural network tuned controller took a shorter time to converge to the target value
by 0.8 seconds confirming that the proposed controller is more effective for the contact force control of the flexible arm.