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Sensors 2016, 16(10), 1709; doi:10.3390/s16101709

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules

Department of Precision Instrument, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 24 August 2016 / Revised: 8 October 2016 / Accepted: 12 October 2016 / Published: 14 October 2016
(This article belongs to the Section Physical Sensors)
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High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial–temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional–integral–derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions. View Full-Text
Keywords: MIMO; self-tuning; temperature control; instrument; high reliability MIMO; self-tuning; temperature control; instrument; high reliability

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, Z.; Ma, C.; Zhu, R. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules. Sensors 2016, 16, 1709.

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