Multiply-connected Neuro PID Control

Kun-Young Han, Hee-Hyol Lee
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Abstract

An ultra-compact binary power plant converts thermal energy into electric power using low temperature difference thermal energy between heat source and cooling source. In control of the binary power plant, changes of characteristic due to environmental condition, corrosion of related equipment and coupling between control loops are the main difficulties in designing a controller and fine-tuning its parameters. In order to realize the stable power generation it is necessary to consider a control system to keep control performance when the changes of characteristic for binary power plant, and to compensate coupling in multi-inputs and multi-outputs (MIMO) systems. A Multiply-Connected (MC) Neuro PID control system using a neural network architecture connected directly by neurons of each control loop is proposed to overcome above difficulties, and its strategy for design of the control system is introduced. The proposed MC Neuro PID control system is compared to traditional PID control systems to show the effectiveness of the MC Neuro PID control through simulations in this paper.
多路连接神经PID控制
超紧凑型双联电厂利用热源和冷却源之间的低温温差热能将热能转化为电能。在双联电厂的控制中,由于环境条件、相关设备的腐蚀以及控制回路之间的耦合而引起的特性变化是控制器设计和参数微调的主要难点。为了实现稳定发电,需要考虑控制系统在双动力系统特性变化时保持控制性能,并对多输入多输出(MIMO)系统进行耦合补偿。针对上述困难,提出了一种采用各控制回路神经元直接连接的神经网络结构的MC神经PID控制系统,并介绍了其控制系统的设计策略。本文通过仿真对比了MC Neuro PID控制系统与传统PID控制系统的对比,验证了MC Neuro PID控制的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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