Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed.

A large part of keeping a process running safely is ensuring that all equipment is functioning well. To do so, routine preventive maintenance needs to be conducted. This paper investigates the feasibility of a functional modeling method-Multilevel Flow Modeling (MFM) extension with roles for modeling of mechanical components functionalities. The method is applied on a case study of a test bench. Consequently, it studies the effects of physical structure failures on functions propagated upwards to objectives. This work can benefit in making the maintenance plan with further experimental study for linking condition monitoring data with the extended MFM models.

This paper describes an automated sewing system that focuses on sewing of curved edge segments. The sewing cell includes an industrial sewing machine as well as three industrial robots that handle the parts during the sewing process. Based on experiences from previous work, several improvements are presented that address issues with previous implementations. The main contribution concerning the control system is a model-based generation of the feed-forward velocity for the robot tool movement, based on a geometric description of the parts. This approach increases performance, especially in the case of curved fabric, as well as robustness in case of faulty sensor data. Additionally, this paper describes hardware upgrades that address mechanical challenges that were described in former publications. Finally, the paper presents experiments comparing the model-based control approach with the former control system based on constant feed-forwards velocity and setpoints.

New challenges are arising in managing power systems as these systems become more complex due to the use of high levels of distributed generation, mainly based on renewable energy sources, and due to the competitive environment within the power sector. At the same time, the use of phasor measurement units (PMUs) provides more information and enables wide-area monitoring with accurate timing. One of the challenges in the near future is converting the high quantity and quality of information provided by PMUs into useful knowledge about operational state of a global system. The use of real-time simulation in closed loop is essential to develop and validate new real-time applications of wide-area PMU data. This paper presents a simulation platform developed within the research project Secure Operation of Sustainable Power Systems (SOSPO). The SOSPO simulation platform (SOSPO-SP) functions in a closed loop integrating new real-time assessment methods to provide useful information to operators in power system control centers, and to develop new control methodologies that handle emergency situations and avoid power system blackouts.

The sewing process is a manufacturing technology which presents severe challenges for automation. Due to variations in the material properties and unpredictable mechanical compliance, real-time sensor-based control strategies are necessary to achieve satisfying results. This paper presents a sewing cell consisting of two lightweight industrial robots and a sewing machine. Sensors are included both for force and edge positioning control. Experiments are presented showing the performance of the proposed real-time control framework. The experiments focus on the real-time control loop for force and edge control during the sewing process.

We present a flexible system that can scan and localize workpieces in 3D for assembly and pick and place operations. The system contains a vision robot that acquires 3D point clouds by performing sweeps with a laser triangulation sensor. Because the sensor is mounted on a robot, we can choose a viewpoint and a scanner trajectory that is optimal for a given task. For example, we can sweep along a semicircular trajectory in order to recognize and grasp parts from a pallet. With the same vision robot, we can perform a sweep of the packaging container in order to recognize and manipulate elements of cardboard. The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose verification and pose refinement that significantly increase the accuracy and robustness of the system. As an application of the system, an industrial prototype workcell is presented. The task is to recognize, grasp and transfer parts of office chairs, such as seats, seat backs and armrests, from a pallet to a cardboard container. It demonstrates how the vision system is easily set up to recognize three different components by using CAD models. The prototype workcell further demonstrates how the pose estimation results can be fed directly to a separate handling robot in order to grasp a chair seat. A series of ten experiments was performed where the chair seat was placed in arbitrary poses in the pallet. Pose estimations were performed in just over one second per experiment, and the obtained accuracy was well within the tolerances for the grasp operation in all ten cases.

This paper presents a study of the real-time control conditions for a robotic system with visual servo control. The system is based on an industrial manipulator with a modified controller allowing real-time joint-level control. The work is particularly concerned with delays and path deviations. The focus is on the Line-of-Sight based path tracking controller. The paper describes an analysis of the different delays in the sensor-robot system and a model is presented based on the overall delay. Further the stability of the modeled system is analyzed in respect to a specific control case. The correctness of the estimated system delay is indicated by comparison of simulated and experimental results.

Safe operation of complex processes requires that operators maintain situational-awareness even in highly automated environments. Automatic reasoning can support operators as well as the automation system itself to react effectively and appropriately to disturbances. However, knowledge-based reasoning about control situations remains a challenge due to the entanglement of process and control systems that co-establish the intended causal structure of a process. Due to this entanglement, reasoning about such systems depends on a coherent representation of control and process. This paper explains modeling of controlled processes with multilevel-flow models and proposes a new framework for modeling causal influence in multilevel flow models on the basis of a flow/potential analogy. The results are illustrated on examples from the domain of electric power systems.