Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3279-3286.

### Control Method of Multi-Split Wireless Remote Monitoring System

Zhihui Zhuanga, Jian Cena, and Shuai Liub,*

1. aSchool of Automatization, Guangdong Polytechnic Normal University, Guangzhou, 510000, China;
bState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, 471000, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: cs.liu.shuai@gmail.com

Abstract: In order to maintain the normal operation of production equipment, achieve the maximum utilization of personnel and the purpose of reducing personnel without reducing efficiency. It is necessary to research the control method of multi-split wireless remote monitoring system. At present, the method of controlling the multi-split wireless remote monitoring system based on fuzzy PID controller is commonly adopted. Firstly, the data of the multi-split wireless remote monitoring system was collected. Secondly, the wavelet transform was used to smooth the data and remove the interference data. Finally, fuzzy PID controller was used to control the multi-split wireless remote monitoring system online. Due to the fuzzy PID controller, although the current method can achieve comprehensive control, large control error and serious network congestion are the main problems. In order to accurately analyze the current network situation and reduce the network delay rate, a method to control the multi-split wireless remote monitoring system based on neuron PID controller was put forward. Firstly, the relevant data of multi-split wireless remote monitoring system were collected. Then, the network prediction function was used to analyze current network situation of the remote monitoring system, and the relevant network data parameters were calculated. According to the calculation results, the neural PID controller was used to achieve the real-time control for the multi-split wireless remote monitoring system. Simulation results show that the proposed method can predict and analyze the current network conditions accurately and avoid network congestion. Thus, real-time control for the remote monitoring system is achieved.