title: Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.
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.
authors.affiliation: Department of Precision Instrument, Tsinghua University, Beijing 100084, China. zhangz14@mails.tsinghua.edu.cn.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Precision Instrument, Tsinghua University, Beijing 100084, China. macheng@mail.tsinghua.edu.cn.
authors.forename: Cheng
authors.initials: C
authors.lastname: Ma
authors.affiliation: Department of Precision Instrument, Tsinghua University, Beijing 100084, China. zr_gloria@mail.tsinghua.edu.cn.
authors.forename: Rong
authors.initials: R
authors.lastname: Zhu
keywords: MIMO; high reliability; instrument; self-tuning; temperature control