Affiliation:
1. School of Computer Science and Engineering Hunan University of Science and Technology XiangTan China
2. Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China
3. Institute of Railway Telecommunication Hunan Technical College of Railway High Speed HengYang China
4. School of Computer Science Colorado Technical University Colorado Springs USA
Abstract
AbstractWith the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.
Funder
Natural Science Foundation of Fujian Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Science Applications
Cited by
1 articles.
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