Sensors 2016, 16(10), 1709; https://doi.org/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
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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)
Abstract
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-TextKeywords:
MIMO; self-tuning; temperature control; instrument; high reliability
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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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