Remarks on model reference self-tuning PID controller using quantum neural network with qubit neurons

Abstract

The control performance of an adaptive controller using a multi-layer quantum neural network comprising qubit neurons as an information processing unit is investigated in this paper. The control system is a self-tuning controller whose control parameters are tuned online by the quantum neural network to track the plant output to follow the desired output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller whose parameters are tuned by the quantum neural network. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate capability and characteristics of the quantum neural self-tuning PID controller. Experimental results show feasibility and effectiveness of the proposed controller.