OROMACS: A Design Framework for Multi-agent Control System

Dao, Phong B.

doi:10.1007/s12555-019-0772-3

Cited by 2 publications

(5 citation statements)

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“…The work in [33] has developed a controller-agent-based design framework (named OROMACS) for the construction of hierarchically structured MACS for mechatronic systems. In OROMACS, the implementation of local controllers is based on the concept of agents, giving the so-called controller-agents; and multicontroller systems thus lead to the concept of the multiagent control system (MACS).…”

Section: Multiagent Control System (Macs)mentioning

confidence: 99%

“…The coordinator determines when and how activities of controller-agents (e.g., calculation of control signals) are applied to the plant. There are five main types of coordination mechanisms (or coordinators), which are essential for the development of MACS [33]:…”

Section: Multiagent Control System (Macs)mentioning

confidence: 99%

“…Using the OROMACS framework [33] we have designed a MACS-based time-index LFFC system for the simulated plant. The hierarchical organization and inside structure of the system is presented in Figures 6 and 7, respectively.…”

Section: Design Of a Macs-based Time-index Lffc Systemmentioning

confidence: 99%

“…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. This combination results in the so-called multiagent control system (MACS) with hierarchical structure.…”

Section: Introductionmentioning

confidence: 99%

“…This combination results in the so-called multiagent control system (MACS) with hierarchical structure. Following the work in [33], this study has proposed a novel solution using the MACS-based design approach for LFFC. The research aims at combining the advantages of adaptive/learning control and agent-based control to form a new multiagent-based LFFC method.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Multiagent Control System (Macs)mentioning

confidence: 99%

Section: Multiagent Control System (Macs)mentioning

confidence: 99%

Section: Design Of a Macs-based Time-index Lffc Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Dao

2021

Energies

Self Cite

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Uma Proposta de Framework para Sistemas de Software de Controle para Plataformas de Robótica Social

Rocha,

Favela,

Muchaluat-Saade

2024

Proceedings of the 30th Brazilian Symposium on Multimedia and the Web (WebMedia 2024)

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There is a gap in open-source robotic platforms that can be customized according to application needs and available resources. Software systems for robots are usually complex due to the need to control multiple sensors and actuators in real time, while simultaneously and asynchronously performing tasks and responding to unexpected situations. In this context, a well-designed architecture that could facilitate building and using robots is desirable. This work proposes a framework for the development of control systems for social robotics platforms. The framework is modular and easily extensible, as it uses an object-oriented approach and adopts the publish-subscribe paradigm for asynchronous message exchange between modules. The framework proposed in this work was instantiated for the design and implementation of two open-source social robots, demonstrating its adaptability and extensibility in different scenarios. We expect that this work will contribute to popularizing the use of low-cost social robots in the areas of education and healthcare.

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OROMACS: A Design Framework for Multi-agent Control System

Cited by 2 publications

References 24 publications

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Uma Proposta de Framework para Sistemas de Software de Controle para Plataformas de Robótica Social

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