Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs

Zhang, Qiang; Liu, Ping; Pannek, Jürgen

doi:10.1080/00207543.2018.1521022

Cited by 19 publications

(2 citation statements)

References 46 publications

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“…Feedback loops guarantee that if any deviation is detected, that information is considered in previous steps to correct the situation. Feedback control solutions for traditional linear production schemes (Bensoussan et al, 2009;Lin et al, 2018) are very popular, but additional proposals for new nonlinear schemes (Spiegler and Naim, 2017;Zhang et al, 2019) may also be found. Furthermore, different studies about how randomness (Garcia et al, 2012), disturbances (Scholz-Reiter et al, 2011), and fluctuations (Yang and Fan, 2016) affect the global behaviour and performance of these feedback control mechanisms have been published.…”

Section: State Of the Artmentioning

confidence: 99%

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Bordel¹,

Alcarria²,

Robles³

2021

Informatica

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Section: State Of the Artmentioning

confidence: 99%

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Bordel¹,

Alcarria²,

Robles³

2021

Informatica

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“…However, PID is mainly used for linear single-input single-output (SISO) systems while it may be intractable for highly nonlinear systems with unpredictable disturbances. In past decades, robust nonlinear control methods were maturely developed and successfully applied to a variety of fields, such as robust nonlinear model predictive control (NMPC) [2][3][4][5], operator-based robust right coprime factorization (RRCF) [6][7][8], and sliding mode control (SMC) control [9][10][11][12][13]. Among them, SMC, also called variable structure control, is one of the most popular robust nonlinear control techniques due to the characteristic of rapid convergence with satisfactory transient performance, insensitivity to model uncertainties and disturbances, and remarkable computational simplicity [10].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Terminal Sliding Mode Tracking Control for a Class of Nonlinear SISO Systems with Input Saturation

et al. 2023

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This paper focuses on the trajectory tracking control for general nonlinear single-input single-output (SISO) systems in which the output is not directly related to the control input. To address the tracking problem with the consideration of possible model uncertainty, external disturbance, and control input saturation, we employ the input-output feedback linearization technique and design a finite-time disturbance observer-based terminal sliding mode controller to improve the tracking performance and enhance the robustness. The stability analysis is carried out by using the Lyapunov method. To alleviate the chattering while achieving an acceptable control performance, a boundary layer method is adopted for the trade-off between the high-frequency control actions and the bounded unavoidable nonzero steady-state error. The proposed method is evaluated on the two typical nonlinear systems, which are fully linearizable and partially linearizable, respectively, and compared to the state-of-the-art method in terms of tracking and robustness through comprehensive numerical simulations. The results show that the proposed method not only renders the estimated disturbance error tends to be zero in finite time, but also has superiority in the fast reaction to disturbance and small tracking error without high-frequency chattering.

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Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

et al. 2021

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Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

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Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs

Cited by 19 publications

References 46 publications

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Disturbance Observer-Based Terminal Sliding Mode Tracking Control for a Class of Nonlinear SISO Systems with Input Saturation

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

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