# Design and development of a distributed and knowledge-based control architecture to achieve operational resilience in cyber-physical production systems

Ali Attajer, Saber Darmoul, Sondès Chaabane, Yves Sallez, Fouad Riane

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Ali Attajer, Saber Darmoul, Sondès Chaabane, Yves Sallez, Fouad Riane. Design and development of a distributed and knowledge-based control architecture to achieve operational resilience in cyber-physical production systems. 7ème Journée Régionale des Doctorants en Automatique, Nov 2021, Compiègne, France. hal-03845298

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# Design and development of a distributed and knowledge-based control architecture to achieve operational resilience in cyber-physical production systems

ATTAJER Ali¹,²,³, DARMOUL Saber¹, CHAABANE Sondes², SALLEZ Yves², RIANE Fouad¹,³

¹Ecole Centrale Casablanca, Bouskoura Ville Verte, 27182, Casablanca, Morocco

ali.attajer, saber.darmoul, fouad.riane}@centrale-casablanca.ma

²LAMIH, UMR CNRS 8201, Université Polytechnique des Hauts-de-France, UPHF, Le Mont Houy, 59313 Valenciennes, France

sondes.chaabane, yves.sallez}@uphf.fr

³IMII, Hassan First University, Settat, Morocco

<figure><img src="image_3.png" /></figure>

## Introduction

### Context

Nowadays, production systems are subject to different types of challenges, related to fast technological evolution, unpredictable markets, changes, disruptions and operational risks [1]. Accordingly, numerous research projects have developed so-called "cyber-physical production systems (CPMS)" (Figure 1). These systems implement new concepts related to technologies that allow the construction of the factories of the future, which can adapt rapidly to unexpected changes, while maintaining or even improving their competitiveness [2]. In the context of a CPMS, resilience refers to the ability of a system to withstand disruptions by maintaining system functions and structures, reducing the magnitude and duration of disrupted states, and/or responding to disruptive events/situations [3].

### Problem statement

With Industry 4.0 technologies, CPMSs can now take advantage of the opportunities for information distribution, and decentralization of decision-making processes that the Industry 4.0 revolution offers [3]. Despite the emergence of distributed/decentralized control paradigms and architectures, such as multi-agent, holonic, and bionic systems [4], the management of operational disruptions and risks is still governed by predominantly centralized or hierarchical organizational modes, information systems, and control architectures that do not take advantage of the full potential and flexibility of Industry 4.0 technologies [5]. In the literature [6], [7], [8], only a limited number of articles focus on the development of distributed architectures, where intelligent products play an active role in the control, management and organization processes of production [9]. Moreover, these references consider only limited types of disturbances and do not propose generic mechanisms, allowing to face several types of disturbances and risks in an autonomous, adaptive and resilient way [10], [11]. To the best of our knowledge, no work has been reported that analyzes the contribution of smart and active products in the operational resilience of the production system in which they are manufactured. Accordingly, the development of distributed control architectures, where smart products are able to cope with different types of disturbances and risks and help decision makers to react in an agile, flexible and resilient manner, remains an open, complex and challenging problem.

### Objective

This project main contribution is the design and development of a distributed and knowledge-based control architecture exploiting product intelligence to achieve resilience, and thus be able to effectively manage disruptions and operational risks in CPMS.

<figure><img src="image_4.png" /></figure>

## Main contribution

In this project, we highlight that distributed decision-making, augmented with white box artificial intelligence, more particularly explicit knowledge representation, captures, stores, uses/reuses, and contributes to confer operational resilience to CPMS. We demonstrate that biological immunity offers a design pattern to develop knowledge-based systems and knowledge management processes, which guarantee embedding operational resilience within CPMS control architectures. Main limitations with respect to the relevant literature, this research project brings answers to are shown in Figure 2.

<figure><img src="image_5.png" /><figcaption>Figure 2. Position and contributions of the research project</figcaption></figure>

## Methods and materials

This work is based on a fundamental assumption, which consists in stipulating that the human Biological Immune System can guide the design of a knowledge-based distributed control architecture to enhance the resilience of operations management and production planning and control in cyber-physical manufacturing systems (Figure 3).

<figure><img src="image_6.png" /><figcaption>Figure 3. Analogies between the biological and manufacturing worlds</figcaption></figure>

The assessment of this assumption is achieved through a knowledge-based decision-making and decentralization of decision mechanisms on intelligent products.

The knowledge-based decision-making is achieved through the development of decision mechanisms, which are based on an innovative combination of Case Based Reasoning (CBR) with an Analytic Hierarchy Process (AHP) augmented with Expert Rules. These decision mechanisms are embedded within Intelligent Products in a decentralized way to enable them learn how to deal with disturbances and operational risks related to resource unavailability and unreliability. It is worth noting that, to the authors' best knowledge, CBR has not yet been implemented as a reaction-based mechanism embedded within Intelligent Products to cope with disturbances in a decentralized way. The suggested architecture is implemented and validated using an ANYLOGIC multi-agent system simulation of an industrial scale learning factory. The estimation of resilience indicators show competitive results and provide interesting research openings.

## Simulation results

A series of simulations scenarios are generated to evaluate the operational resilience using an ANYLOGIC multi-agent system simulation of an industrial scale learning factory: S.mart plateform of LAMIH, Valenciennes. For instance, Figure 4 provides radar charts for a set of scenarios. In each radar chart, 3 performance indicators are displayed for three reaction strategies: (1) the average throughput time of products ; (2) the average production cost ; (3) the average products throughput per hour. Results highlight that the augmented AHP outperforms as the product fully takes advantages of the system's flexibilities.

<figure><img src="image_7.png" /><figcaption>Figure 4. Results of simulations</figcaption></figure>

## Conclusion

The research project is based on Biological Immune System to guide the design of a knowledge-based distributed control architecture that enhances the resilience of operations management and production planning and control in CPMS. It suggests an immune inspired distributed product-driven control architecture to cope with operational disturbances using two novel decision mechanisms, which are an innovative combination of Case Based Reasoning (CBR) with an adaptive version of the Analytic Hierarchy Process (AHP) augmented Expert Rules. Methodological and numerical results are presented, and promising perspectives are highlighted.

## Références

[1]H. Panetto, B. Iung, D. Ivanov, G. Weichhart, and X. Wang, "Challenges for the cyber-physical manufacturing enterprises of the future," Annual Reviews in Control, vol. 47, pp. 200-213, 2019, doi: 10.1016/j.arcontrol.2019.02.002.

[2]A. Napoleone, M. Macchi, and A. Pozzetti, "A review on the characteristics of cyber-physical systems for the future smart factories," Journal of Manufacturing Systems, vol. 54, no. December 2019, pp. 305-335, 2020, doi: 10.1016/j.jmsy.2020.01.007.

[3]M. Moghaddam and A. Deshmukh, "Resilience of cyber-physical manufacturing control systems," Manufacturing Letters, vol. 20, pp. 40-44, 2019, doi: 10.1016/j.mfglet.2019.05.002.

[4]D. Trentesaux, "Distributed control of production systems," Engineering Applications of Artificial Intelligence, vol. 22, no. 7, pp. 971-978, 2009, doi: 10.1016/j.engappai.2009.05.001.

[5]W. Shen, Q. Hao, H. J. Yoon, and D. H. Norrie, "Applications of agent-based systems in intelligent manufacturing: An updated review," Advanced Engineering Informatics, vol. 20, no. 4, pp. 415-431, Oct. 2006, doi: 10.1016/j.aei.2006.05.004.

[6]J. Barbosa, P. Leitao, D. Trentesaux, A. W. Colombo, and S. Karnouskos, "Cross Benefits from Cyber-Physical Systems and Intelligent Products for Future Smart Industries," 2016 IEEE 14th International Conference on Industrial Informatics (Indin), no. July, pp. 504-509, 2016.

[7]D. Trentesaux and A. Thomas, "Product-Driven Control: Concept, Literature Review and Future Trends," in Service Orientation in Holonic and Multi Agent Manufacturing and Robotics, vol. 472, T. Borangiu, A. Thomas, and D. Trentesaux, Eds. Springer Berlin Heidelberg, 2013, pp. 135-150. doi: 10.1007/978-3-642-35852-4.

[8]Attajer, A., Darmoul, S., Riane, F., & Bouras, A. (2019). Distributed Maintenance: A Literature Analysis and Classification. IFAC-PapersOnLine, 52(13), 619-624.

[9]N. Bayar, S. Darmoul, S. Hajri-Gabouj, and H. Pierreval, "Fault detection, diagnosis and recovery using Artificial Immune Systems: A review," Engineering Applications of Artificial Intelligence, vol. 46, pp. 43-57, Nov. 2015, doi: 10.1016/j.engappai.2015.08.006.

[10]O. Cardin, D. Trentesaux, A. Thomas, P. Castagna, T. Berger, and H. Bril El-Haouzi, "Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges," Journal of Intelligent Manufacturing, vol. 28, no. 7, pp. 1503-1517, 2017, doi: 10.1007/s10845-015-1139-0.

[11]W. Derigent, D. Mcfarlane, and H. B. El-haouzi, "Intelligent products through a SOHOMA prism," in Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA 2020., 2020, vol. 952, pp. 367-384. doi: https://doi.org/10.1007/978-3-030-69373-2_26.

[12]Attajer, A., Darmoul, S., Chaabane, S., Riane, F., & Sallez, Y. (2020). Benchmarking Simulation Software Capabilities Against Distributed Control Requirements: FlexSim vs AnyLogic. In SOHOMA (pp. 520-531).