Programmable Logic Controllers in the Context of Industry 4.0

Sehr, Martin A.; Lohstroh, Marten; Weber, Matthew; Ugalde, Ines; Witte, Martin; Neidig, Joerg; Hoeme, Stephan; Niknami, Mehrdad; Lee, Edward A.

doi:10.1109/tii.2020.3007764

Cited by 51 publications

(16 citation statements)

References 37 publications

(42 reference statements)

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“…Programmable Logic Controllers (PLCs) have been widely used in the industrial sector as the base element for controlling and monitoring field components and production processes. Not many researches talk about this powerful controller [ 49 ]. PLCs have been successfully established as the platform of choice for industrial process outline and factory automation conducted by the so-called IEC 61131 standard [ 50 ].…”

Section: Short Theory and Background Overviewmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

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Section: Short Theory and Background Overviewmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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“…The programmable logic controller (PLC) designed to perform logic functions has been widely employed in various process industries [8,9,10]. The PLC's use offers several advantages, including flexibility, reliability, low power consumption and ease of expansion.…”

Section: B Interoperability Issuesmentioning

confidence: 99%

Development of a Real-time Framework between MATLAB and PLC through OPC-UA: A case study of a microgrid energy management system

Zerk

Ouassaid

Zidani

2022

Preprint

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Many research laboratories, which develop advanced algorithms for controlling Renewable Energy Systems (RES), lack specialized equipment to carry out research activities and perform experiments. Real-time simulation platforms are suitable for designing specific applications such as fast or real-time computation models. In addition, these platforms provide fast, accurate and reliable studies for RES control implementation. However, Real-time platforms are too expensive and not affordable for most laboratories. In this context, a cost-effective platform is designed to assist researchers in their activities. The hardware solution consists of using a Programmable Logic Control (PLC) to acquire data from the various sensors and control the system through actuators and PID controllers. From a software standpoint, a communication framework between MATLAB and the PLC is developed using the OPC-UA (Open Platform Communications Unified Architecture) technology. In this paper, the operation and efficiency of the platform have been tested using the Software in the Loop (SIL) technique. Hence, the operating model is designed using MATLAB/Simulink to simulate and analyze the appropriate control technique suitable for PLC implementation, whereas the control is executed through Siemens S7-1200 PLC using TIA PORTAL. A visualization interface is set up using WinCC-RT. Furthermore, this paper describes the design of the platform and discusses its utilization, providing examples of its usage through a case study. Indeed, a complex algorithm developed in MATLAB/Simulink can be automatically executed by any PLC using the designed framework.

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“…Programmable Logic Controllers (PLCs) have been widely used in the industrial sector as the base element for controlling and monitoring field components and production processes. Not many researches talk about this powerful controller [55]. PLCs have been successfully established as the platform of choice for industrial process outline and factory automation conducted by the so-called IEC 61131 standard [57].…”

Section: Programmable Logic Controllers (Plc) Functions In the Era Of Industry 40mentioning

confidence: 99%

“…In summary, a PLC system contains three main sections: a programming section, a networking section, and a physical section. The physical section comprises the inputs segment (sensors) and the outputs segment (actuators) [55].…”

Section: Programmable Logic Controllers (Plc) Functions In the Era Of Industry 40mentioning

confidence: 99%

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

Kiangala

Wang

2021

Preprint

View full text Add to dashboard Cite

The industrial manufacturing sector is currently undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer a static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. Operators are subject to frequent safety inductions to react in emergencies but very little is done for robots. Our research proposes a safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from emergency stops (ESTOPs) button. The factory emergency signal can be given by an ESTOP or a voice command sent directly to the factory central controller: an S7-1200 Siemens programmable logic controller (PLC) in this experiment. We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations.

show abstract

Programmable Logic Controllers in the Context of Industry 4.0

Cited by 51 publications

References 37 publications

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Development of a Real-time Framework between MATLAB and PLC through OPC-UA: A case study of a microgrid energy management system

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

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