Remarks on Quaternion Neural Network-Based Self-Tuning PID Controller

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In this study, we present an investigation of the control performance of an adaptive controller using a multi-layer 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 discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the quaternion neural network-based self-tuning PID controller. Experimental results show the feasibility and effectiveness of the proposed controller.