Reliable Industrial IoT-Based Distributed Automation

Reconfigurable manufacturing systems supported by Industrial Internet-of-Things (IIoT) are modular and easily integrable, promoting efficient system/component reconfigurations with minimal downtime. Industrial systems are commonly based on sequential controllers described with Control Interpreted Petri Nets (CIPNs). Existing design methodologies to distribute centralized automation/control tasks focus on maintaining functional properties of the system during the process, while disregarding failures that may occur during execution (e. g., communication packet drops, sensing or actuation failures). Consequently, in this work, we provide a missing link for reliable IIoT-based distributed automation. We introduce a method to transform distributed control models based on CIPNs into Stochastic Reward Nets that enable integration of realistic fault models (e. g., probabilistic link models). We show how to specify desired system properties to enable verification under the adopted communication/fault models, both at design-and run-time; we also show feasibility of runtime verification on the edge, with a continuously updated system model. Our approach is used on real industrial systems, resulting in modifications of local controllers to guarantee reliable system operation in realistic IIoT environments

Reliable Industrial IoT-Based Distributed Automation

Reconfigurable manufacturing systems supported by Industrial Internet-of-Things (IIoT) are modular and easily integrable, promoting efficient system/component reconfigurations with minimal downtime. Industrial systems are commonly based on sequential controllers described with Control Interpreted Petri Nets (CIPNs). Existing design methodologies to distribute centralized automation/control tasks focus on maintaining functional properties of the system during the process, while disregarding failures that may occur during execution (e. g., communication packet drops, sensing or actuation failures). Consequently, in this work, we provide a missing link for reliable IIoT-based distributed automation. We introduce a method to transform distributed control models based on CIPNs into Stochastic Reward Nets that enable integration of realistic fault models (e. g., probabilistic link models). We show how to specify desired system properties to enable verification under the adopted communication/fault models, both at design-and run-time; we also show feasibility of runtime verification on the edge, with a continuously updated system model. Our approach is used on real industrial systems, resulting in modifications of local controllers to guarantee reliable system operation in realistic IIoT environments

Security Analysis for Distributed IoT-Based Industrial Automation

Internet of Things (IoT) technologies enable development of reconfigurable manufacturing systems--a new generation of modularized industrial equipment suitable for highly customized manufacturing. Sequential control in these systems is largely based on discrete events, whereas their formal execution semantics is specified as control interpreted Petri nets (CIPN). Despite industry-wide use of programming languages based on the CIPN formalism, formal verification of such control applications in the presence of adversarial activity is not supported. Consequently, in this article, we introduce security-aware modeling and verification techniques for CIPN-based sequential control applications. Specifically, we show how CIPN models of networked industrial IoT controllers can be transformed into time Petri net (TPN)-based models and composed with plant and security-aware channel models in order to enable system-level verification of safety properties in the presence of network-based attacks. Additionally, we introduce realistic channel-specific attack models that capture adversarial behavior using nondeterminism. Moreover, we show how verification results can be utilized to introduce security patches and facilitate design of attack detectors that improve system resiliency and enable satisfaction of critical safety properties. Finally, we evaluate our framework on an industrial case study

Towards Plug-n-Play Numerical Control for Reconfigurable Manufacturing Systems

Modern manufacturing systems require fast and effective adaptation to fluctuating market conditions and product diversification. This high level adaptability can be achieved through the utilization of Reconfigurable Manufacturing Systems (RMS), which should be based on modular equipment that is easily integrated, scalable, convertible in terms of functionality, and self diagnosable. RMS also necessitate the use of a dynamic controller architecture that is distributed, fully modular, and self configurable. In this paper, we present a control system design approach for reconfigurable machine tools through the use of modularized and decentralized CNC control. Specifically, we investigate design challenges for Plug-n-Play automation systems, where new system functionalities, such as adding new axes in existing CNC units, can be introduced without significant reconfiguration efforts and downtime costs. We propose a fully decentralized motion control architecture realized through a network of individual axis control modules. Reconfiguration of motion control systems based on this architecture can be achieved by only presenting the controller on each axis with information about machine configuration and the type of axis. This effectively enables modularity, reconfigurability, and interoperability of the machine control system. Finally, we present an implementation of the decentralized architecture based on the use of a real-time operating system, wireless networking, and low-cost ARM Cortex-M3 MCUs; we illustrate its effectiveness by considering machining of a standard test part defined in ISO 10791-7 using a software-in-the-loop testbed

Synchronization of Distributed Controllers in Cyber-Physical Systems

Due to misaligned clock sources, distributed control in Cyber-Physical Systems (CPS) requires not only synchronous execution of control algorithms on distributed system components, which we refer to as cyber-synchronization, but also appropriate generation of actuation signals-we refer to this as physical-synchronization. In this paper, we define general requirements for cyber-physical synchronization, as well as show their use on a specific real-world application-distributed motion control for reconfigurable manufacturing systems. We present synchronization challenges in such systems and investigate effects of synchronization errors on the overall system functionality (i.e., machining accuracy). Furthermore, we introduce a low-cost synchronization scheme that can be implemented with of-the-shelf components and validate it on standardized accuracy tests with 2D configurations of industry-grade single-axis robots. We show that our cyber-physical synchronization techniques ensure minimal accuracy impairment of distributed motion control without introducing significant cost/overhead to system design

Synchronization of Distributed Controllers in Cyber-Physical Systems

Due to misaligned clock sources, distributed control in Cyber-Physical Systems (CPS) requires not only synchronous execution of control algorithms on distributed system components, which we refer to as cyber-synchronization, but also appropriate generation of actuation signals-we refer to this as physical-synchronization. In this paper, we define general requirements for cyber-physical synchronization, as well as show their use on a specific real-world application-distributed motion control for reconfigurable manufacturing systems. We present synchronization challenges in such systems and investigate effects of synchronization errors on the overall system functionality (i.e., machining accuracy). Furthermore, we introduce a low-cost synchronization scheme that can be implemented with of-the-shelf components and validate it on standardized accuracy tests with 2D configurations of industry-grade single-axis robots. We show that our cyber-physical synchronization techniques ensure minimal accuracy impairment of distributed motion control without introducing significant cost/overhead to system design

Towards Plug-n-Play Numerical Control for Reconfigurable Manufacturing Systems

Modern manufacturing systems require fast and effective adaptation to fluctuating market conditions and product diversification. This high level adaptability can be achieved through the utilization of Reconfigurable Manufacturing Systems (RMS), which should be based on modular equipment that is easily integrated, scalable, convertible in terms of functionality, and self diagnosable. RMS also necessitate the use of a dynamic controller architecture that is distributed, fully modular, and self configurable. In this paper, we present a control system design approach for reconfigurable machine tools through the use of modularized and decentralized CNC control. Specifically, we investigate design challenges for Plug-n-Play automation systems, where new system functionalities, such as adding new axes in existing CNC units, can be introduced without significant reconfiguration efforts and downtime costs. We propose a fully decentralized motion control architecture realized through a network of individual axis control modules. Reconfiguration of motion control systems based on this architecture can be achieved by only presenting the controller on each axis with information about machine configuration and the type of axis. This effectively enables modularity, reconfigurability, and interoperability of the machine control system. Finally, we present an implementation of the decentralized architecture based on the use of a real-time operating system, wireless networking, and low-cost ARM Cortex-M3 MCUs; we illustrate its effectiveness by considering machining of a standard test part defined in ISO 10791-7 using a software-in-the-loop testbed