Neural network for the forward kinematics problem in parallel manipulator

Abstract

The parallel manipulator's unique structure presents an interesting problem in its forward kinematics solution, which involves the solving of a series of simultaneous nonlinear equations. The ability of a neural network to recognize the relationship between the input values and the output values of a system without fully understanding the system was fully exploited in this case. With the simple inverse kinematics solution of the manipulator, a neural network was trained to solve the forward kinematics of the parallel manipulator quite accurately. By adjusting the offset of the result obtained, the neural network is able to achieve an accuracy of 0.1 mm and 0.5 degrees for the six output values. >