Kinematics Control of Redundant Manipulators Using CMAC Neural Network

摘要

The inverse kinematics problems of redundant manipulators have been investigated for many years. The conventional method of solving this problem analytically is by applying the Jacobian Pseudoinverse Algorithm. It is effective and able to resolve the redundancy for additional constraints. However, its demand for computational effort makes it not suitable for real-time control. Recently, neural networks have been widely used in robotic control because they are fast, fault-tolerant and able to learn. In this paper, we will present the application of CMAC (Cerebellar Model Articulation Controller) neural network for solving the inverse kinematics problems in real time. Simulations will be carried out for evaluating the performance of the CMAC neural network.