Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems

Abstract

In this study, we investigate the control performance of an adaptive controller using a multilayer quaternion neural network. The control system is a self-tuning controller, the control parameters of which are tuned online by the quaternion neural network to track plant output to follow the desired output generated by a reference model. A proportional-integral-derivative (PID) controller is used as a conventional controller, the parameters of which are tuned by the quaternion neural network. Computational experiments to control a single-input single-output (SISO) discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the quaternion-neural-networkbased self-tuning PID controller. Experimental results show the feasibility and effectiveness of the proposed controller.