Date: Thursday 12 March 2020
Time: 16:00 - 17:30
Location / Room: Bayard
Chair:
Ulrike Thomas, Chemnitz University of Technology, DE
Co-Chair:
Robert De Simone, INRIA, FR
Modeling and design of transportation and manufacturing systems from a cyber-physical system (CPS) perspective have lately attracted extensive attention and the session covers various aspects, from modelling of traffic intersections and control of traffic signals, to implementations of iterative learning controllers for control blocks. Other contributions deal with the selection of network architectures for manufacturing plants and the Digital Twin of production processes for validation.
| Time | Label | Presentation Title Authors |
|---|---|---|
| 16:00 | 12.5.1 | CPS-ORIENTED MODELING AND CONTROL OF TRAFFIC SIGNALS USING ADAPTIVE BACK PRESSURE Speaker: Wanli Chang, University of York, GB Authors: Wanli Chang1, Debayan Roy2, Shuai Zhao1, Anuradha Annaswamy3 and Samarjit Chakraborty2 1University of York, GB; 2TU Munich, DE; 3Massachusetts Institute of Technology, US Abstract Modeling and design of automotive systems from a cyber-physical system (CPS) perspective have lately attracted extensive attention. As the trend towards automated driving and connectivity accelerates, strong interactions between vehicles and the infrastructure are expected. This requires modeling and control of the traffic network in a similarly formal manner. Modeling of such networks involves a tradeoff between expressivity of the appropriate features and tractability of the control problem. Back-pressure control of traffic signals is gaining ground due to its decentralized implementation, low computational complexity, and no requirements on prior traffic information. It guarantees maximum stability under idealistic assumptions. However, when deployed in real traffic intersections, the existing back-pressure control algorithms may result in poor junction utilization due to (i) fixed-length control phases; (ii) stability as the only objective; and (iii) obliviousness to finite road capacities and empty roads. In this paper, we propose a CPS-oriented model of traffic intersections and control of traffic signals, aiming to address the utilization issue of the back-pressure algorithms. We consider a more realistic model with transition phases and dedicated turning lanes, the latter influencing computation of the pressure and subsequently the utilization. The main technical contribution is an adaptive controller that enables varying-length control phases and considers both stability and utilization, while taking both cases of full roads and empty roads into account. We implement a mechanism to prevent frequent changes of control phases and thus limit the number of transition phases, which have negative impact on the junction utilization. Microscopic simulation results with SUMO on a 3x3 traffic network under various traffic patterns show that the proposed algorithm is at least about 13% better in performance than the existing fixed-length back-pressure control algorithms reported in previous works. This is a significant improvement in the context of traffic signal control. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:30 | 12.5.2 | NETWORK SYNTHESIS FOR INDUSTRY 4.0 Speaker: Enrico Fraccaroli, Università di Verona, IT Authors: Enrico Fraccaroli, Alan Michael Padovani, Davide Quaglia and Franco Fummi, Università di Verona, IT Abstract Today's factory machines are ever more connected together and with SCADA, MES, ERP applications as well as external systems for data analysis. Different types of network architectures must be used for this purpose. For instance, control applications at the lowest level are very sensitive to delays and errors while data analysis with machine learning procedures requires to move large amount of data without real-time constraints. Standard data formats, like Automation Markup Language (AML), have been established to document factory environment, machine placement and network deployment, however, no automatic technique is currently available in the context of Industry 4.0 to choose the best mix of network architectures according to spacial constraints, cost and performance. We propose to fill this gap by formulating an optimization problem. First of all, spatial and communication requirements are extracted from the AML description. Then, the optimal interconnection of wired or wireless channels is obtained according to application objectives. Finally, this result is back-annotated to AML to be used in the life cycle of the production system. The proposed methodology is described through a small, but complete, smart production plant. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 17:00 | 12.5.3 | PRODUCTION RECIPE VALIDATION THROUGH FORMALIZATION AND DIGITAL TWIN GENERATION Speaker: Stefano Spellini, Università di Verona, IT Authors: Stefano Spellini1, Roberta Chirico1, Marco Panato1, Michele Lora2 and Franco Fummi1 1Università di Verona, IT; 2Singapore University of Technology and Design, SG Abstract The advent of Industry 4.0 is making production processes every day more complicated. As such, early process validation is becoming crucial to avoid production errors thus decreasing costs. In this paper, we present an approach to validate production recipes. Initially, the recipe is specified according to the ISA-95 standard, while the production plant is described using AutomationML. These specifications are formalized into a hierarchy of assume-guarantee contracts. Each contract specifies a set of temporal behaviors, characterizing the different machines composing the production line, their actions and interaction. Then, the formal specifications provided by the contracts are systematically synthesized to automatically generate a digital twin for the production line. Finally, the digital twin is used to evaluate, and validate, both the functional and the extra-functional characteristics of the system. The methodology has been applied to validate the production of a product requiring additive manufacturing, robotic assembling and transportation. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 17:15 | 12.5.4 | PARALLEL IMPLEMENTATION OF ITERATIVE LEARNING CONTROLLERS ON MULTI-CORE PLATFORMS Speaker: Mojtaba Haghi, Eindhoven University of Technology, NL Authors: Mojtaba Haghi1, Yusheng Yao2, Dip Goswami1 and Kees Goossens2 1Eindhoven University of Technology, NL; 2Eindhoven university of technology, NL Abstract This paper presents design and implementation techniques for iterative learning controllers (ILCs) targeting predictable multi-core embedded platforms. Implementation on embedded platforms results in a number of timing artifacts. Sensor-to-actuator delay (referred to as delay) is an important timing artifact which influences the control performance by changing the dynamic behavior of the system. We propose a delay-based design for ILCs that identifies and operates in the performance-optimal delay region. We then propose two implementation methods -- sequential and parallel -- for ILCs targeting the predictable multi-core platforms. The proposed methods enable the designer to carefully adjust the scheduling to achieve the optimal delay region in the resulting control system. We validate our results by the hardware-in-the-loop (HIL) simulation, considering a motion system as a case-study. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 17:30 | End of session | |