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

Agent-Based Distributed Resource Allocation in Continuous Dynamic Systems

Intelligent agents and multiagent systems reveal new strategies to design highly flexible automation systems. There are first promising industrial applications of multiagent systems for the control of manufacturing, logistics, traffic or multi-robot systems. One reason for the success of most of these applications is their nature as some form of a distributed resource allocation problem which can be addressed very well by multiagent systems. Resource allocation problems solved by agents can be further categorized into static or dynamic problems. In static problems, the allocations do not depend on time and many resource allocation problem of practical interest can be solved using these static considerations, even in discrete-event systems like manufacturing or logistic systems. However, problems especially in highly dynamic environments cannot be addressed by this pure static approach since the allocations, i.e. the decision variables, depend on time and previous states of the considered system. These problems are hardly considered in the relevant agent literature and if, most often only discrete-event systems are considered. This work focuses on agent-based distributed dynamic resource allocation problems especially in continuous production systems or other continuous systems. Based on the current states of the distributed dynamic system, continuous-time allocation trajectories must be computed in real-time. Designing multiagent systems for distributed resource allocation mainly comprises the design of the local capabilities of the single agents and the interaction mechanisms that makes them find the best or at least a feasible allocation without any central control. In this work, the agents are designed as two-level entities: while the low-level functions are responsible for the real-time allocation of the resources in the form of closed-loop feedback control, the high-level functionalities realize the deliberative capabilities such as long-term planning and negotiation of the resource allocations. Herein, the resource allocation problem is considered as a distributed optimization problem under certain constraints. The agents play the role of local optimizers which then have to coordinate their local solutions to an overall consistent solution. It is shown in this contribution that the described approach can be interpreted as a market-based allocation scheme based on balancing of supply and demand of the resources using a virtual price. However, the agents calculate and negotiate complete supply and demand trajectories using model-based predictions which also leads to the calculation of a price trajectory. This novel approach does not only consider the dynamic behaviour of the distributed system but also combines control tasks and resource allocation in a very consistent way. The approach is demonstrated using two practical applications: a heating system and an industrial sugar extraction process

Research on intelligent controller design for MIMO spatially -Distributed systems with applications

Spatially dynamic distributed systems have been attracting increasing attention from researchers in the field of system modelling and control since their introduction as an alternative to simple systems to meet the ever-greater requirements to make industrial systems more precise and energy-efficient and to overcome process complexities. An approach whereby complex systems with multi-dimensional parameters, inputs or outputs are simply disregarded or simplified with the help of convenient mathematical models is no longer feasible. Therefore, the purpose of the present study is to contribute to the advancement of both theoretical and empirical knowledge in this field through the means of theoretical analysis, application simulations and case studies. From a theoretical perspective, this study focuses primarily on the design methodology of control systems. To this end, the first step is identification of requirements from the applications, followed by the implementation of an original approach underpinned by data prediction for type-2 T-S fuzzy control with the purpose of making the control system design more convenient. With this aim in mind, the study creates an interface/platform to link or anticipate spatially dynamic distributed system output from lumped system data by taking advantage of the threedimensional character of type-2 fuzzy sets. Moreover, on the basis of a decoupled spatially dynamic distributed system, this study applies Mamdani-type and interval type-2 T-S type fuzzy control, and extends a discussion about the results of simulation and analysis. With regard to application examination, the study contributes to primarily with system analysis and modelling. Along with the progress of physical analysis, a MIMO model is customized for the plant by expanding from the lumped physical character to a distributed system. Furthermore, the coupling feature of the object is addressed based on the decoupling approach and the pole placement approach, while the SISO approach is expanded to a universally acknowledged MIMO approach and Matlab is used to produce the simulation results.As a conclusion, in this research, firstly a state space model was established to expand the SISO system into a MIMO system and the interacted inputs and outputs have been decoupled using decoupling method; and then a Mamdani-type fuzzy control was designed for temperature control and an Interval Type-2 fuzzy control was designed for pressure control, using a simple state-space model instead of a fuzzy model, accordance with the practical plant in use, and very satisfied, very robust control performances were obtained

A framework for Model-Driven Engineering of resilient software-controlled systems

AbstractEmergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones

A model driven component agent framework for domain experts

Industrial software systems are becoming more complex with a large number of interacting parts distributed over networks. Due to the inherent complexity in the problem domains, most such systems are modified over time to incorporate emerging requirements, making incremental development a suitable approach for building complex systems. In domain specific systems it is the domain experts as end users who identify improvements that better suit their needs. Examples include meteorologists who use weather modeling software, engineers who use control systems and business analysts in business process modeling. Most domain experts are not fluent in systems programming and changes are realised through software engineers. This process hinders the evolution of the system, making it time consuming and costly. We hypothesise that if domain experts are empowered to make some of the system cha nges, it would greatly ease the evolutionary process, thereby making the systems more effective. Agent Oriented Software Engineering (AOSE) is seen as a natural fit for modeling and implementing distributed complex systems. With concepts such as goals and plans, agent systems support easy extension of functionality that facilitates incremental development. Further agents provide an intuitive metaphor that works at a higher level of abstraction compared to the object oriented model. However agent programming is not at a level accessible to domain experts to capitalise on its intuitiveness and appropriateness in building complex systems. We propose a model driven development approach for domain experts that uses visual modeling and automated code generation to simplify the development and evolution of agent systems. Our approach is called the Component Agent Framework for domain-Experts (CAFnE), which builds upon the concepts from Model Driven Development and the Prometheus agent software engineering methodolo gy. CAFnE enables domain experts to work with a graphical representation of the system, which is easier to understand and work with than textual code. The model of the system, updated by domain experts, is then transformed to executable code using a transformation function. CAFnE is supported by a proof-of-concept toolkit that implements the visual modeling, model driven development and code generation. We used the CAFnE toolkit in a user study where five domain experts (weather forecasters) with no prior experience in agent programming were asked to make changes to an existing weather alerting system. Participants were able to rapidly become familiar with CAFnE concepts, comprehend the system's design, make design changes and implement them using the CAFnE toolkit

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (Rt), maximum percentage overshoot (MPOS), settling time (Ts), and steady state error (Ess) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems

Constructive tool design for formal languages : from semantics to executing models

Embedded, distributed, real-time, electronic systems are becoming more and more dominant in our lives. Hidden in cars, televisions, mp3-players, mobile phones and other appliances, these hardware/software systems influence our daily activities. Their design can be a huge effort and has to be carried out by engineers in a limited amount of time. Computer-aided modelling and design automation shorten the design cycle of these systems enabling companies to deliver their products sooner than their competitors. The design process is divided into different levels of abstraction, starting with a vague product idea (abstract) and ending up with a concrete description ready for implementation. Recently, research has started to focus on the system level, being a promising new area at which the product design could start. This dissertation develops a constructive approach to building tools for system-level design/description/modelling/specification languages, and shows the applicability of this method to the system-level language POOSL (Parallel Object-Oriented Specification Language). The formal semantics of this language is redefined and partly redeveloped, adding probabilistic features, real-time, inheritance, concurrency within processes, dynamic ports and atomic (indivisible) expressions, making the language suitable for performance analysis/modelling. The semantics is two-layered, using a probabilistic denotational semantics for stating the meaning of POOSL’s data layer, and using a probabilistic structural operational semantics for the process layer and architecture layer. The constructive approach has yielded the system-level simulation tool rotalumis, capable of executing large industrial designs, which has been demonstrated by two successful case studies—an ATM-packet switch (in conjunction with IBM Research at Z¨urich) and a packet routing switch for the Internet (in association with Alcatel/Bell at Antwerp). The more generally applicable optimisations of the execution engine (rotalumis) and the decisions taken in its design are discussed in full detail. Prototyping, where the system-level model functions as a part of the prototype implementation of the designed product, is supported by rotalumis-rt, a real-time variant of the execution engine. The viability of prototyping is shown by a case study of a learning infrared remote control, partially realised in hardware and completed with a system-level model. Keywords formal languages / formal specification / modelling languages / systemlevel design / embedded systems / real-time systems / performance analysis / discrete event simulation / probabilistic process algebra / design automation / prototyping / simulation tool

Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays

[EN] In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n-input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under the Projects DPI2012-31303 and DPI2014-55932-C2-2-R.Aranda-Escolástico, E.; Salt Llobregat, JJ.; Guinaldo, M.; Chacon, J.; Dormido, S. (2018). Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays. Sensors. 18(5):1-19. https://doi.org/10.3390/s18051491S119185Mansano, R., Godoy, E., & Porto, A. (2014). The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems. Sensors, 14(12), 24441-24461. doi:10.3390/s141224441Shao, Q. M., & Cinar, A. (2015). System identification and distributed control for multi-rate sampled systems. Journal of Process Control, 34, 1-12. doi:10.1016/j.jprocont.2015.06.010Albertos, P., & Salt, J. (2011). Non-uniform sampled-data control of MIMO systems. Annual Reviews in Control, 35(1), 65-76. doi:10.1016/j.arcontrol.2011.03.004Cuenca, A., & Salt, J. (2012). RST controller design for a non-uniform multi-rate control system. Journal of Process Control, 22(10), 1865-1877. doi:10.1016/j.jprocont.2012.09.010Cuenca, Á., Ojha, U., Salt, J., & Chow, M.-Y. (2015). A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System. Information Sciences, 321, 31-47. doi:10.1016/j.ins.2015.05.035Kalman, R. E., & Bertram, J. E. (1959). General synthesis procedure for computer control of single-loop and multiloop linear systems (an optimal sampling system). Transactions of the American Institute of Electrical Engineers, Part II: Applications and Industry, 77(6), 602-609. doi:10.1109/tai.1959.6371508Khargonekar, P., Poolla, K., & Tannenbaum, A. (1985). Robust control of linear time-invariant plants using periodic compensation. IEEE Transactions on Automatic Control, 30(11), 1088-1096. doi:10.1109/tac.1985.1103841Bamieh, B., Pearson, J. B., Francis, B. A., & Tannenbaum, A. (1991). A lifting technique for linear periodic systems with applications to sampled-data control. Systems & Control Letters, 17(2), 79-88. doi:10.1016/0167-6911(91)90033-bLi, D., Shah, S. L., Chen, T., & Qi, K. Z. (2001). Application of dual-rate modeling to CCR octane quality inferential control. IFAC Proceedings Volumes, 34(25), 353-357. doi:10.1016/s1474-6670(17)33849-1Salt, J., & Albertos, P. (2005). Model-based multirate controllers design. IEEE Transactions on Control Systems Technology, 13(6), 988-997. doi:10.1109/tcst.2005.857410Nemani, M., Tsao, T.-C., & Hutchinson, S. (1994). Multi-Rate Analysis and Design of Visual Feedback Digital Servo-Control System. Journal of Dynamic Systems, Measurement, and Control, 116(1), 45-55. doi:10.1115/1.2900680Sim, T. P., Lim, K. B., & Hong, G. S. (2002). Multirate predictor control scheme for visual servo control. IEE Proceedings - Control Theory and Applications, 149(2), 117-124. doi:10.1049/ip-cta:20020238Xinghui Huang, Nagamune, R., & Horowitz, R. (2006). A comparison of multirate robust track-following control synthesis techniques for dual-stage and multisensing servo systems in hard disk drives. IEEE Transactions on Magnetics, 42(7), 1896-1904. doi:10.1109/tmag.2006.875353Wu, Y., Liu, Y., & Zhang, W. (2013). A Discrete-Time Chattering Free Sliding Mode Control with Multirate Sampling Method for Flight Simulator. Mathematical Problems in Engineering, 2013, 1-8. doi:10.1155/2013/865493Salt, J., & Tomizuka, M. (2014). Hard disk drive control by model based dual-rate controller. Computation saving by interlacing. Mechatronics, 24(6), 691-700. doi:10.1016/j.mechatronics.2013.12.003Salt, J., Casanova, V., Cuenca, A., & Pizá, R. (2013). Multirate control with incomplete information over Profibus-DP network. International Journal of Systems Science, 45(7), 1589-1605. doi:10.1080/00207721.2013.844286Liu, F., Gao, H., Qiu, J., Yin, S., Fan, J., & Chai, T. (2014). Networked Multirate Output Feedback Control for Setpoints Compensation and Its Application to Rougher Flotation Process. IEEE Transactions on Industrial Electronics, 61(1), 460-468. doi:10.1109/tie.2013.2240640Khargonekar, P. P., & Sivashankar, N. (1991). 2 optimal control for sampled-data systems. Systems & Control Letters, 17(6), 425-436. doi:10.1016/0167-6911(91)90082-pTornero, J., Albertos, P., & Salt, J. (2001). Periodic Optimal Control of Multirate Sampled Data Systems. IFAC Proceedings Volumes, 34(12), 195-200. doi:10.1016/s1474-6670(17)34084-3Kim, C. H., Park, H. J., Lee, J., Lee, H. W., & Lee, K. D. (2015). Multi-rate optimal controller design for electromagnetic suspension systems via linear matrix inequality optimization. Journal of Applied Physics, 117(17), 17B506. doi:10.1063/1.4906588LEE, J. H., GELORMINO, M. S., & MORARIH, M. (1992). Model predictive control of multi-rate sampled-data systems: a state-space approach. International Journal of Control, 55(1), 153-191. doi:10.1080/00207179208934231Mizumoto, I., Ikejiri, M., & Takagi, T. (2015). Stable Adaptive Predictive Control System Design via Adaptive Output Predictor for Multi-rate Sampled Systems∗∗This work was partially supported by KAKENHI, the Grant-in-Aid for Scientific Research (C) 25420444, from the Japan Society for the Promotion of Science (JSPS). IFAC-PapersOnLine, 48(8), 1039-1044. doi:10.1016/j.ifacol.2015.09.105Carpiuc, S., & Lazar, C. (2016). Real-Time Multi-Rate Predictive Cascade Speed Control of Synchronous Machines in Automotive Electrical Traction Drives. IEEE Transactions on Industrial Electronics, 1-1. doi:10.1109/tie.2016.2561881Roshany-Yamchi, S., Cychowski, M., Negenborn, R. R., De Schutter, B., Delaney, K., & Connell, J. (2013). Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks. IEEE Transactions on Control Systems Technology, 21(1), 27-39. doi:10.1109/tcst.2011.2172444Donkers, M. C. F., Tabuada, P., & Heemels, W. P. M. H. (2012). Minimum attention control for linear systems. Discrete Event Dynamic Systems, 24(2), 199-218. doi:10.1007/s10626-012-0155-xQuevedo, D. E., Ma, W.-J., & Gupta, V. (2015). Anytime Control Using Input Sequences With Markovian Processor Availability. IEEE Transactions on Automatic Control, 60(2), 515-521. doi:10.1109/tac.2014.2335311Aranda Escolastico, E., Guinaldo, M., Cuenca, A., Salt, J., & Dormido, S. (2017). Anytime Optimal Control Strategy for Multi-Rate Systems. IEEE Access, 5, 2790-2797. doi:10.1109/access.2017.2671906Guinaldo, M., Sánchez, J., & Dormido, S. (2017). Control en red basado en eventos: de lo centralizado a lo distribuido. Revista Iberoamericana de Automática e Informática Industrial RIAI, 14(1), 16-30. doi:10.1016/j.riai.2016.09.007Van Loan, C. (1977). The Sensitivity of the Matrix Exponential. SIAM Journal on Numerical Analysis, 14(6), 971-981. doi:10.1137/0714065Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends® in Optimization, 2(3-4), 157-325. doi:10.1561/2400000013Sala, A., Cuenca, Á., & Salt, J. (2009). A retunable PID multi-rate controller for a networked control system. Information Sciences, 179(14), 2390-2402. doi:10.1016/j.ins.2009.02.017Chacon, J., Saenz, J., Torre, L., Diaz, J., & Esquembre, F. (2017). Design of a Low-Cost Air Levitation System for Teaching Control Engineering. Sensors, 17(10), 2321. doi:10.3390/s1710232

Model-Driven Development of Control Applications: On Modeling Tools, Simulations and Safety

Control systems are required in various industrial applications varying from individual machines to manufacturing plants and enterprises. Software applications have an important role as an implementation technology in such systems, which can be based on Distributed Control System (DCS) or Programmable Control System (PLC) platforms, for example. Control applications are computer programs that, with control system hardware, perform control tasks. Control applications are efficient and flexible by nature; however, their development is a complex task that requires the collaboration of experts and information from various domains of expertise.This thesis studies the use of Model-Driven Development (MDD) techniques in control application development. MDD is a software development methodology in which models are used as primary engineering artefacts and processed with both manual work and automated model transformations. The objective of the thesis is to explore whether or not control application development can benefit from MDD and selected technologies enabled by it. The research methodology followed in the thesis is the constructive approach of design science.To answer the research questions, tools are developed for modeling and developing control applications using UML Automation Profile (UML AP) in a model-driven development process. The modeling approach is developed based on open source tools on Eclipse platform. In the approach, modeling concepts are kept extendable. Models can be processed with model transformation techniques that plug in to the tool. The approach takes into account domain requirements related to, for example, re-use of design. According to assessment of industrial applicability of the approach and tools as part of it, they could be used for developing industrial DCS based control applications.Simulation approaches that can be used in conjunction to model-driven development of control applications are presented and compared. Development of a model-in-the-loop simulation support is rationalized to enable the use of simulations early while taking into account the special characteristics of the domain. A simulator integration is developed that transforms UML AP control application models to Modelica Modeling Language (ModelicaML) models, thus enabling closed-loop simulations with ModelicaML models of plants to be controlled. The simulation approach is applied successfully in simulations of machinery applications and process industry processes.Model-driven development of safety applications, which are parts of safety systems, would require taking into account safety standard requirements related to modeling techniques and documentation, for example. Related to this aspect, the thesis focuses on extending the information content of models with aspects that are required for safety applications. The modeling of hazards and their associated risks is supported with fault tree notation. The risk and hazard information is integrated into the development process in order to improve traceability. Automated functions enable generating documentation and performing consistency checks related to the use of standard solutions, for example. When applicable, techniques and notations, such as logic diagrams, have been chosen so that they are intuitive to developers but also comply with recommendations of safety standards