Low-Cost and High-Efficiency Electromechanical Integration for Smart Factories of IoT with CNN and FOPID Controller Design under the Impact of COVID-19

Abstract

This study proposes a design for unmanned chemical factories and implementation based on ultra-low-cost Internet of Things technology, to combat the impact of COVID-19 on industrial factories. A safety and private blockchain network architecture was established, including a three-layer network structure comprising edge, fog, and cloud calculators. Edge computing uses a programmable logic controller and a single-chip microcomputer to transmit and control the motion path of a four-axis robotic arm motor. The fog computing architecture is implemented using Python software. The structure is integrated and applied using a convolutional neural network (CNN) and a fractional-order proportional-integral-derivative controller (FOPID). In addition, edge computing and fog computing signals are transmitted through the blockchain, and can be directly uploaded to the cloud computing controller for signal integration. The integrated application of the production line sensor and image recognition based on the network layer was addressed. We verified the image recognition of the CNN and the robot motor signal control of the FOPID. This study proposes that a CNN + FOPID method can improve the efficiency of the factory by more than 50% compared with traditional manual operators. The low-cost, high-efficiency equipment of the new method has substantial contribution and application potential.

Keywords

• smart factory
• IoT
• machine learning
• robot control
• COVID-19