Towards the use of bond graphs for manufacturing control: Design of controllers

Sagawa, Juliana Keiko; Mušič, Gasper

doi:10.1016/j.ijpe.2019.03.017

Cited by 16 publications

(3 citation statements)

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“…This will offer other echelons stability and efficient operations in the supply chain networks. Therefore, management optimization for a manufacturer is one of the main topics discussed in many articles [8,16,24,26]. With the increasing complexity of supply chain networks, management software for decision-making assists in eliminating errors, completing business tasks, and reporting activities.…”

Section: Production Management Using System Dynamicsmentioning

confidence: 99%

“…Therefore, the independent production system, separated from the complete supply chain, has been discussed by many articles to identify how structure and decision policies can generate system behaviors of manufacturers. Sagawa and Mušič [16] conducted bond graph/mathematical models to describe multi-workstation production systems and proposed state feedback controllers using linear quadratic regulator (LQR). AL-Khazraji et al [17] simulated the dynamical behaviors of a production-inventory system and then applied control system theory to improve system performance.…”

Section: Introductionmentioning

confidence: 99%

“…The selection of appropriate control methods for decision-making must be in accordance with specific system characteristics. Some control strategies have been well-recognized in supply chain management, such as synchronization and suppression of chaotic supply chain systems by feedback controllers and Lyapunov stability theory [8,16,23]. A robust intelligent controller is employed based on sliding mode control theory, and radial basis functions neural networks [24].…”

Section: Introductionmentioning

confidence: 99%

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Dynamical analysis and decision support system of production management

Cuong,

Kim,

Long

et al. 2024

RAIRO-Oper. Res.

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Nonlinear system dynamics and feedback control theory are presented for management optimization of supply chain system. Linearization and simplification method are widely used in analyzing system dynamics of supply chains because actual production models are highly complex and nonlinear systems. With the advanced system dynamics, it is possible directly to deal with nonlinear dynamical problems without linear approximate methods so that the decision makers can obtain more accurate results for systematic management strategies. This paper aims to propose nonlinear system theory to explore dynamical behavior and control synthesis of production-distribution system by utilizing Forrester’s model. A novel super twisting sliding mode control (SWT-SMC) algorithm has been presented based on adaptation law, ensuring management optimization against disruptions. The closed-loop system stability has been guaranteed by using Lyapunov theory. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the adaptive super twisting sliding mode control (ASWT-SMC) algorithm. Four types of decision criteria have been employed to compare system performance between control strategies. With superb decision scheme powered by control algorithm, novel supply chain software can learn an ever-fluctuating production flow and anticipate the need for changes in real market.

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Section: Production Management Using System Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%