scholarly journals Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1945
Author(s):  
Hsu-Chih Huang ◽  
Jing-Jun Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Control ◽  
Physical System ◽  
Fuzzy Systems ◽  
Online Control ◽  
Polar Space ◽  
Robotic System ◽  
Lyapunov Theory ◽  
Flower Pollination Algorithm ◽  
Cyber Physical System

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both the fuzzy structure and the number of IF–THEN rules are optimized through the FPA evolutionary process. This approach overcomes the drawback of the structure tuning problem in conventional fuzzy systems. After deriving the polar-space kinematics model of Mecanum vehicles, an optimal EML FPA-fuzzy online control scheme is synthesized, and the global stability is proven via Lyapunov theory. An embedded cyber-physical robotic system is then constructed using the typical 5C strategy. The proposed FPA-fuzzy computation collaborates with the advanced sensors and actuators of Mecanum vehicles to design a cyber-physical robotic system. Compared with conventional Cartesian-space control methods, the proposed EML FPA-fuzzy has the advantages of metaheuristics, fuzzy online control, and cyber-physical system design in polar coordinates. Finally, the mechatronic design and experimental setup of a Mecanum vehicle cyber-physical system is constructed. Through experimental results and comparative works, the effectiveness and merit of the proposed methods are validated. The proposed EML FPA-fuzzy control approach has theoretical and practice significance in terms of its real-time capability, online parameter tuning, convergent behavior, and hybrid artificial intelligence.

scholarly journals Hardware and Software Infrastructure of Digital Twin Technology

2020 ◽  
Author(s):  
Zakharov L.A ◽  
Derksen L.A.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Physical System ◽  
Digital Model ◽  
Cyber Physical System ◽  
Product Lifecycle ◽  
Software Infrastructure ◽  
Digital Twin ◽  
Data Product ◽  
Basic Approaches

This article describes of hardware and software infrastructure that provides the implementation of digital double technology. The basic approaches to determining the technologies that make up the infrastructure for the implementation of the digital twin, as well as the benefits of implementing this technology are considered. The need for processing and storing big data, as well as the benefits of implementing this technology, is substantiated. Keywords: digital twin, digital model, big data, product lifecycle, cyber-physical system, automation, machine learning, smart maintenance.

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

Reverse Mentoring the Editing Edge in Management 4.0

2020 ◽  
pp. 185-193
Author(s):  
Swati Sisodia ◽  
Neetima Agarwal
Keyword(s):  
Machine Learning ◽  
Generation X ◽  
Physical System ◽  
Industry 4.0 ◽  
Business Processes ◽  
Cyber Physical System ◽  
Logistic Management ◽  
Ingenious Method

Industry 4.0 is based on the implementation of a cyber-physical system, which includes sensors, networks, computers, offering digital enhancement and well-coordinated activities. This would create a great pool of all the workforce generations, having diverse experience, agility, and different modes of working. Millennials would add more of machine learning and Generation X and Y would be the richest source of tacit and operational knowledge. Together, they would develop solutions for catering complex and networked production and aggressive logistic management, meeting the challenges of the Industry 4.0. However, the benefits of digitization and automation can be achieved, if the different generations of workforce collaborate, cooperate, and postulate together in all the business processes. Reverse mentoring is a pristine concept and ingenious method to empower learning and encourage cross-generational connections. This chapter would elaborate on the advantage of reverse mentoring in crafting Industry 4.0 more acrobatic and quick-moving.

A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications

2018 ◽  
Vol 15 ◽  
pp. 139-142 ◽  
Author(s):  
Peter O'Donovan ◽  
Colm Gallagher ◽  
Ken Bruton ◽  
Dominic T.J. O'Sullivan
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Industry 4.0 ◽  
Fog Computing ◽  
Low Latency ◽  
Cyber Physical System

scholarly journals Cyber Physical System Considering Physical Contacts in Robotic Manipulation for Improving Automation in Food Industry

2021 ◽  
pp. 23-28
Author(s):  
Zhongkui Wang ◽  
◽  
Masao Shimizu ◽  
Sadao Kawamura
Keyword(s):  
Physical System ◽  
Food Industry ◽  
Population Aging ◽  
Physical Space ◽  
Robotic System ◽  
Robotic Manipulation ◽  
Cyber Physical System ◽  
Multiple Sensors ◽  
Cyber Space ◽  
Network Platform

Population aging in Japan is exacerbating the labor shortage problem in food industry, agriculture, and other labor-intensive industries. Recently, automation and IoT technologies become highly demanded in such industries for improving productivity and labor-saving. This article reviews the challenges of introducing automation in food industry and proposes a cyber physical system framework for applications in food industry. For facilitating the application of IoT technologies, we propose a module integrated with multiple sensors for monitoring the environment and the robotic system conditions. Further, a network platform is proposed to connect the physical space (robotic system) and the cyber space (cloud). Two examples of robotic systems for automatic tempura plating and presentation and chopped green onion topping are presented to demonstrate the capabilities of applying such technologies in food industry.

Machine Learning and Big Data in Cyber‐Physical System: Methods, Applications and Challenges

2021 ◽  
pp. 49-91
Author(s):  
Janmenjoy Nayak ◽  
P. Suresh Kumar ◽  
Dukka Karun Kumar Reddy ◽  
Bighnaraj Naik ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Physical System ◽  
Cyber Physical System
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