Remarks on Feedforward–Feedback Controller Using a Trained Quaternion Neural Network Based on Generalised ℍℝ Calculus and Its Application to Controlling a Robot Manipulator
Kazuhiko Takahashi, Eri Tano, Masafumi Hashimoto · 2021
In this study, a gradient–descent method extended to quaternion numbers, in which the generalised Hamiltonian– Real calculus is used to calculate derivatives of a real function with respect to quaternion variables, is explored. It was used to derive a training algorithm of a quaternion neural network that functions as an adaptive–type controller in control systems applications. A feedforward–feedback controller is designed based on the quaternion neural network. Furthermore, computational experiments on trajectory tracking control of a three–link robot manipulator are conducted to verify the learning capability and the characteristics of the quaternion neural network–based controller. The experimental results showed the quaternion neural network’s feasibility and capability for a control systems application.