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This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called "Hot air tunnel". The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]