Distributed multi-agent scheduling and control system for robotic flexible assembly cells

Maoudj, Abderraouf; Bouzouia, Brahim; Hentout, Abdelfetah; Kouider, Ahmed; Toumi, R.

doi:10.1007/s10845-017-1345-z

Cited by 38 publications

(17 citation statements)

References 28 publications

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“…A food traceability network physical system method based on intelligent value stream was proposed to improve the efficiency of the food traceability system [182]. Maoudj et al [183] developed a distributed multi-agent system for scheduling and controlling robotic flexible assembly units. The production data are collected in real time for product traceability based on the intelligent logistics system developed by the radio frequency identification technology [184][185][186][187][188], realizing the production management and dynamic scheduling of the workshop.…”

Section: Logisticsmentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Section: Logisticsmentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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“…Metaheuristics such as genetic algorithms, tabu search, particle swarm optimization, etc. provide better results than heuristics for some scheduling applications (Maoudj et al, 2019;Ouelhadj & Petrovic, 2009;Xiong & Fu, 2018). However, most metaheuristic algorithms are non-deterministic and require long solution times for large problem sizes (Maoudj et al, 2019).…”

Section: Choosing An Appropriate Machine Learning Algorithmmentioning

confidence: 99%

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

et al. 2021

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In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

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“…Over the last two decades, multiagent system (MAS) technology has been suggested as a promising solution for solving complex control problems in manufacturing and mechatronic systems, as discussed in [24][25][26][27][28][29][30][31][32], because of its ability to provide many advantages such as reusability, robustness, flexibility, adaptability, and scalability. In the research [33], the concept of agent from the field of MAS has been merged with the concept of controller of the control engineering discipline to form the concept of controller-agent.…”

Section: Introductionmentioning

confidence: 99%

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Dao

2021

Energies

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Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

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Distributed multi-agent scheduling and control system for robotic flexible assembly cells

Cited by 38 publications

References 28 publications

Digital twin-based sustainable intelligent manufacturing: a review

Digital twin-based sustainable intelligent manufacturing: a review

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

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