Self-tuning PID controller using quantum neural network with qubit-inspired neurons

Article type: Research Article

Authors: Takahashi, Kazuhiko^{a; *} | Shiotani, Yuka^a | Hashimoto, Masafumi^b

Affiliations: [a] Information Systems Design, Doshisha University, Kyoto, Japan | [b] Intelligent Information Engineering and Science, Doshisha University, Kyoto, Japan

Correspondence: [*] Corresponding author: Kazuhiko Takahashi, Information Systems Design, Doshisha University, Kyoto, Japan. E-mail: katakaha@mail.doshisha.ac.jp

Abstract: This paper investigates the performance of an adaptive controller using a multi-layer quantum neural network (QNN) comprising qubit-inspired neurons as information processing units. The control system is a self-tuning controller whose control parameters are tuned online by the QNN to track plant output relative to the desired plant output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller, with its parameters tuned by the QNN. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate the capability and characteristics of the quantum neural self-tuning controller. The experiment results demonstrate the feasibility and effectiveness of the proposed controller.

Keywords: Quantum neural network, qubit-inspired neuron, self-tuning controller, PID controller, adaptive control

DOI: 10.3233/HIS-140204

Journal: International Journal of Hybrid Intelligent Systems, vol. 12, no. 1, pp. 41-52, 2015