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The neural network based robotic arm control concept covers three areas, decentralized adaptive joint control, an inverse kinematics, and path planning. Included are new results from the decentralized adaptive joint controller. This joint controller uses neural networks to adapt a proportional-integral-derivative (PID)/PVA controller. The results show that neural networks allow for fast, accurate control. The authors have tested the joint controller in a robotic testbed simulation software. The neural driven inverse kinematic system has produced accurate performance. The results show that the overall system outperforms conventional methods.>
Cox et al. (Thu,) studied this question.
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