Search

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

This paper concerns the global cooperative output regulation problem for a class of nonlinear multi-agent systems with unknown control directions and unknown exosystem. The communication among agents makes it difficult to implement the Nussbaum gain technique to handle the non-identical unknown control directions. Moreover, the unknown parameter in the exosystem, which cannot be easily handled by the estimators in existing results, makes our problem more intricate. To overcome these challenges, we employ a novel internal model candidate to develop a two-layer control scheme for an adaptive distributed controller design. Theoretic stability analysis is presented and a numerical example is given to show the effectiveness of the proposed controller.

In this paper we consider Model Predictive Control (MPC) design for roll angle control for a Fixed-Wing Unmanned Aerial Vehicle (UAV) with Multiple Segmented Control Surfaces. The challenge of roll angle control for a Fixed-Wing UAV consists of switching between inner and outer aileron-pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid-control problem as a switched linear constrained MPC-QP problem and switched state observer design for Fixed-Wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation target, which is to stay below the sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good choice for real-time roll angle control for Fixed-Wing UAVs., In this paper we consider model predictive control (MPC) design for roll angle control for a fixed-wing unmanned aerial vehicle (UAV) with multiple segmented control surfaces. The challenge of roll angle control for a fixed-wing UAV consists of switching between inner and outer aileron pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid control problem as a switched linear constrained MPC-QP problem and switched state observer design for fixed-wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good solution for real-time roll angle control of fixed-wing UAVs.

This paper proposes an efficient method for wind power generation forecasting by Long Short Term Memory (LSTM) of Deep Neural Network (DNN). It is one of recurrent neural networks that make use of past output of the network, but replaces hidden layers of the conventional networks with the LSTM Block with memory and three gates of input, output and forget. Artificial and Deep Neural Networks are inclined to overfit leaning data in learning process. This paper proposes a modified LSTM that considers to prevent LSTM from overfitting with two strategies. One is Dropout to exclude some nodes randomly and change network topology while the other is Weight Decay that evaluates smaller weights between neurons. The effectiveness of the proposed method is demonstrated for real data of wind power generation.

This paper addresses the challenges of developing an embedded non-linear model predictive control (NMPC) solution for the optimal driving of miniature scale autonomous vehicles (AVs). The NMPC approach lends itself perfectly to driving applications, provided that a system for localization and tracking of the vehicle is available. An important challenge in the implementation results from the need to accurately steer the vehicle at high speeds, which requires fast actuation. In this paper we present a solution to this problem, which employs an artificial neural network (ANN) controller trained with rigorous NMPC input-output data. We discuss the development process, from modelling until the realization of the ANN controller within the operating system of the AV. The procedure is demonstrated within the virtual environment of the popular F1/10 race car, an AV platform widely used in AI and autonomous driving challenges. The results contain both NMPC and ANN-based simulations for different race tracks and for different driving strategies. The main focus of this work lies in the formulation of the optimal driving control problem and the training method of the ANN. Our approach uses a standardization of the driving problem, which enables us to abstractize optimal driving and to simplify it for the learning process. We show how driving patterns can be learned accurately on a reduced set of training data and that they can subsequently be extended to new and more challenging driving situations.

This paper proposes an event-triggered switched control system (ET-SCS) scheme for the implementation of moving target defense (MTD) control strategy in cyber-physical system (CPS). The proposed scheme uses the ET-SCS to obfustace the system structure/appearance while at the same time renders the closed loop CPS trajectories stable in the presence of cyber intrusion on the CPS actuator. The paper develops a mechanism for detecting the presence of such intrusions and then shows the asymptotic stability of the closed loop CPS.

In this paper we present a simple software tool, designed as a game, that can be used to make (especially primary) school students aware of the importance of control systems. An overhead crane model is used as an effective dynamic system to show the difficulty of applying manual control in some contexts and how a control system can help to achieve the required performance. In addition to the technical details of the tool, which has been developed in Matlab, the overall experience with the students is explained in the paper and some preliminary results of its use with people without expertise in control are discussed.

The paper presents the development of a digital twin for a high frequency hardening robot and connected hardware and software modules. The paper describes the virtual environment model, the robot emulation and optimization model, and the reference generation model, as well as their respective visual interfaces, used for controlling both the physical and digital robots. The application is integrated into the multi-purpose Virtual Intelligent Portable Robot Platform (VIPRO).

This paper presents the control of an underactuated four Cable-Driven Parallel Robot (CDPR) using a modified input-output feedback linearization technique. The effect of internal dynamics (due to the underactuated degrees of freedom of the CDPR) on the behavior of the moving platform is presented to highlight the need of an improved controller to stabilize the system outputs. A modified control scheme is then proposed as a solution to obtain stable system outputs. A structure with two separate branches is modeled to simultaneously act on the control inputs and the mathematical calculations are done using the well-established equations of nonlinear control theory. Following this, the response of the system to the modified control law is then verified by simulation. A comparison between the classical and modified feedback linearization is shown to illustrate the significant improvement in the stabilization of the various parameters such as the cable tensions and platform orientations.

International audience; This paper investigates the sampled-data control of continuous-time Takagi-Sugeno (T-S) fuzzy systems. The closed-loop dynamics is rewritten as a T-S system with input time-varying delays. In this context, asynchronous membership functions appears in the closed-loop dynamics. Thus, to reduce the conservatism of design conditions involving mismatch membership functions, a dedicated relaxation scheme is proposed. Then, from a convenient Lyapunov-Krasovskii function and the application of the Finsler's Lemma, new LMI-based conditions are proposed for the design of sampled-data Parallel-Distributed-Compensation (PDC) controllers. An example is provided to illustrate the effectiveness of the proposed design methodology in simulation, as well as to highlight their conservatism improvement regarding to previous related results from the literature.

A low-complexity anti-windup compensation scheme for linear parameter-varying (LPV) controllers is proposed in this paper. Anti-windup compensation usually increases complexity of LPV controllers significantly. A synthesis algorithm is used in this paper that, unlike conventional algorithms, splits the problem into an observer synthesis and a subsequent state feedback synthesis. The resulting controller structure is exploited for a novel differential implementation that allows straightforward incorporation of conventional anti-windup logics. The method is used to design a pitch-axis flight control law for an unmanned aerobatic aircraft, where anti-windup compensation is an important practical requirement. Applicability is demonstrated in nonlinear simulation using a flight-test-validated high-fidelity model.

Many undergraduate engineering students have just a single introductory feedback control course in their study list. Often the curricula found in such courses is totally based on continuous time-domain classic control techniques. However, currently most control design techniques are implemented in digital machines. Thus, digital control concepts should be covered in introductory control courses. In this paper, the issue of how to implement and test digital industrial controllers is addressed. Teaching experiments based on the APMonitor temperature control lab (TCLab) are proposed. It will be shown that TCLab as an Arduino based portable kit, provides an excellent means to test digital controllers, as it is a compact and portable lab to be used by lecturers and students. While there are many low-cost and portable hardware options for teaching dynamics and control, a novel aspect of this paper is the digital control education methods that are validated with classroom experience, particularly with Biomedical and Bioengineering students. Preliminary results are presented.

In this paper, there is presented the problem of assessing the manufacturability of complex products at the design stage of the manufacturing process considering the criterion of execution of assembly operations processes and production costs. Assessment of manufacturability was given on the example of the Boothroyd & Dewhurst method. This paper describes how to estimate the times and costs of assembly operations according to the mentioned above criteria with the use of the expert system. The presented example illustrates its practical application. The method of analysis presented in this article, developed by the authors, will be the subject of further research aimed at creation of an advanced expert system supporting the design of assembly systems.

The subject of the paper is the mathematical model to manage the logistics-maintenance process of overhead type cranes operated in critical systems. The study case of the paper is a hot rolling mills system of a steel plant with critical overhead cranes operating with hazard conditions and continuous operation. The model output is an optimal overhead cranes maintenance-task distribution minimizing the production line risk stopped and the model input is a digital database with historical worker-orders related with the operation, maintenance-logistics and management process of the hot rolling mills plant. The mathematical model behind the maintenance task making-decision process is a stochastic no-linear optimization model with bounded constraint that evaluates a risk global-system behaviour indicator based on Monte Carlo simulations. The definition and description volume of work in this investigation is extensive, reason why the current paper focusses the attention on model definition and dataflow concepts, specifically data collection, filtering, analysis, and storage from different sources, leaving open the application and numerical results for future works.

In this paper, a project oriented course on guidance and control of autonomous aerial vehicles (UAVs) is presented. The paper describes the different modules of the course and how they are addressed using the project oriented approach. The project uses a quadrotor UAV that is used as the case study during the course. In this course, the students learn how to mathematically model quadrotor UAV flight characteristics, develop feedback control algorithms to enable stable flight control and fuse sensor measurements using Kalman filter techniques to estimate the UAV position and orientation. Students develop these concepts through both simulation and interaction with UAV real measurements. Throughout the course, students build a full 6-degree-of-freedom simulation of controlled quadrotor UAV flight using MATLAB and Simulink.

The presented paper deals with the area of research on multibody mechanical systems focused on underactuated systems, where the impact of the missing actuator is compensated by means of mathematical modeling. Several approaches based on the dynamics of individual bodies make it possible to obtain the mathematical model of the mechanical system. This paper focuses on summarizing and comparing Lagrange’s and Kane’s method, describing the steps that need to be applied to obtain a model for a particular mechanical system in each case. The methods are subsequently applied to selected benchmark underactuated systems such as cart-pole, acrobot, and reaction wheel pendulum, where the aim is to show that both produce the same equations of motion. The corresponding procedures are implemented in MATLAB as part of a custom application with graphical user interface.

This paper deals with a topology for a class of interconnected systems, referred to as a highly interconnected system, consisting of interconnected plants and local controllers. We address the respective cyber attack surfaces as well as a design approach for detection and isolation of covert cyber attacks. For each pair of plant and controller, a cyber attack is implemented by a malicious agent, and its detection and isolation are achieved by associating the controller with two observers. These observers estimate the states of the plant, and compare the estimated states to determine if a neighbouring plant is under a covert cyber attack. The paper presents the modelling of the topology, the analysis of the covertness of cyber attacks, the design approach for the detection and isolation as well as a required existence condition. Simulation results are provided for the application of the design approach to interconnected pendula systems that are subject to a covert cyber attack.

It is well-known that students learn substantially more if a lecture is complemented by active inquiry-based activities and problem solving than from only passively listening to a lecture. Naturally, this requires the teacher to include elements into the course such as laboratories, group projects, tutorials and interactive e-learning modules. For the most effective teaching concept, all such components of the course work are tightly intertwined and work together towards the same goal and learning objectives. Therefore, we show in this paper how Matlab apps can be included into the wider effort of learning facilitation in an introductory automatic control course. Among other advantages, Matlab apps can offer an unlimited number of exercises and examples that can be generated automatically, they offer interactive interfaces that can include gamification aspects such as levels and highscore lists to increase the motivation, and they can be done at the students’ own pace. The course evaluation and the feedback from students at the University of Stuttgart suggest that the inclusion of the Matlab apps presented in this paper is indeed a success.

Recently, a new technique for identification of Discrete-Event Systems (DES) with the aim of fault detection has been proposed in the literature, where a model is obtained from the observation of the fault-free system behavior. In some cases, the system may execute different tasks in a sequential order to perform the complete operation of the system. In these cases, the number of observed paths representing the complete system operation may grow exponentially with the number of tasks. In addition, by using black-box identification methods, it is possible that the sequential order that the tasks must be performed is not represented in the model, reducing the fault detection capability or delaying the fault detection. In this paper, a two-level hierarchical approach for DES identification is proposed. In the higher level of the model hierarchy, the system is described by using some basic knowledge of its functioning provided by an expert, and in the lower level of the hierarchy, the behavior is described by black-box identified models for the system tasks. The modeling framework proposed in this paper reduces the number of observed paths needed for system identification and increases the fault detection capability. A practical example is used to illustrate the results of the paper.

Recent findings in research on malware threats indicate an increasing use of information hiding techniques as a novel approach for compromising IT-Systems by using covert functions and hidden channels. Especially in the context of covert intrusion and data exfiltration, networks of Industrial Control Systems (ICS) are a valuable target for information hiding based attacks. In this paper we discuss how 18 known patterns of information hiding in networks can be applied to protocols found in ICS networks and demonstrate how information can be covertly embedded and retrieved at the example of Modbus/TCP to achieve an overall protocol-compliance by also studying the embedding capacity. Additionally we provide a first tendency of warden- compliance, if the warden has a suspicion that there is a hidden message (conspicuousness). For a practical analysis we introduce an evaluation framework based on open-source software enabling assessment and quick implementation of hidden channels in common protocols found in ICS networks. In combination with a pattern-based approach for the identification of hidden channels, we show how this framework can be utilized as a systematic approach to identify and evaluate plausible hidden channels in industrial communication protocols. From the 14 identified patterns, in this paper we use the introduced framework to implement and evaluate two exemplary timing and storage channels in Modbus/TCP. Our results show 14 protocol-compliant patterns of information hiding based attacks in the context of Industrial Control Systems as well as the necessity of more research in this particular field, especially in terms of further plausible combination of pattern, warden-compliance, detection and mitigation strategies.

Data selection is a critical issue in data-driven soft sensor design. The paper proposes a new method for data selection based on a feature extraction step, followed by data selection algorithms. The method has been applied to an industrial case study, i.e., the estimation of the quality of processed wastewater produced by a Sour Water Stripping plant working in a refinery. The paper reports the results obtained with different data selection algorithms. The comparison has been performed both by using raw data and the feature extraction phase.

As the industry continues to pursue the reduction of downtimes and increases in the efficiency of resource usage, data appears as a valuable business asset, empowering intelligent automation solutions. In order to deal with current challenges in acquisition and management of data in industrial settings, this paper proposes a framework for intelligent automation. The traditional automation pyramid does not cope with today’s application requirements, as intelligent automation needs to exchange, in realtime, information with all layers of the pyramid, simultaneously. The middleware for intelligent automation provides interoperability through integration software along with cloud functionalities and big data warehousing. The middleware also provides decentralization by connecting to remote decision support systems. This paper presents two real implementation examples of the framework in the wood and the chemical industry. The framework successfully addresses the requirements of key intelligent automation features and data characteristics, supporting the development of support decision systems into automation modules.

This paper proposes a scheme for distributing a group of Unmanned Aerial Vehicles (UAVs) in an environment using Broadcast Control (BC) algorithm. BC is a recent technique that uses stochastic optimization to control a multi-agent system for accomplishing a global task. BC’s variant, the Pseudo-perturbation based BC (PBC), uses model based predicted decision and shows faster convergence than BC. In this paper, we have modified the existing BC schemes by incorporating relevant constraints to consider physical limitations of the agents. The proposed scheme is applied to a group of UAVs having kinematic constraints for an area coverage task. Results from numerical simulation show that the performance of BC methods are maintained, and more importantly, the designed path constraints for the UAVs produces a less erratic, continuous and realistic travel path as the UAVs fly to reach their goal.

Varying production regimes and loading conditions on equipment often result in multiple operating modes in process operations. The data recorded from such processes will typically be multimodal in nature leading to challenges in applying standard data-driven process monitoring approaches. Moreover, even if a monitoring approach is able to account for the variability present in a training set comprised of historical process data, in order to be robust and reliable the method will need to account for any new operating modes which might emerge during production. Therefore, it is desirable to have a monitoring algorithm that can both handle data multimodality in off-line training and, when implemented on-line, can actively update in order to incorporate new operating modes. This paper proposes a monitoring framework which combines an unsupervised clustering approach with a kernel-based Multivariate Statistical Process Monitoring (MSPM) algorithm. A monitoring model is trained off-line and is subsequently used to detect anomalies on-line. An anomaly might be indicative of either a developing fault or a change in the process to a new operating mode. In the latter case, the monitoring model can be updated to account for the new mode whilst still being able to detect faults under this framework. The advantages of the off-line training procedure relative to a standard kernel-based method are demonstrated via a numerical simulation. Additionally, the monitoring performance in the presence of faults and the capability of updating the model in the presence of new operating modes is demonstrated using a benchmark data set from an experimental pilot plant.

A scientific and reasonable recommendation algorithm for plant-protecting UAV (Unmanned Aerial Vehicle) operating helps UAV renters with optimal and economical solutions, as well improves the efficiency of plant protecting work and its management. This paper proposed a method of UAV operation recommendation for plant-protecting UAV users based on log data processing, and built a log data real-time analysis and calculation system using Apache Storm framework technology. According to the characteristics of the plant protection operations and the UAV users, two recommendation algorithms were designed and developed in this paper, one of them combined user feature and collaborative filtering and the other one was based on content filtering. In the former algorithm, the most similar neighbor users of the specific target user were investigated first, then the score of every similar neighbor’s each operation was calculated and an appropriate operation recommendation list was produced according to the calculation result of their weighted scores. The later recommendation algorithm took the target user’s interested operation location, spraying type and plant type into consideration, and it recommended the potential interested plant-protecting operations for the target user by analyzing his/her online search, browse history and records. These two algorithms were developed and integrated into an existing system we built earlier using Python. System test and analysis results showed that Storm framework could provide a real-time, low latency, high throughput and robust computing framework, and the log data processing based algorithms could give users the most meaningful plant-protecting operation recommendations according to the preset rules, which could potentially improve the system’s intellectuality, convenience and usability.

ABC classification is a technique widely used by companies to deal with inventories consisting of very large numbers of distinct stock keeping units. Single-criterion ABC classification methods are often used in practice and recently multi-criteria methods have attracted the attention of academics and practitioners. Several models have been developed to deal with the multi-criteria ABC inventory classification (MCIC). To the best of our knowledge, very few researches have used the unsupervised machine learning methods to address the MCIC problem despite their attractive theoretical and practical properties. Therefore, in this paper, the Gaussian mixture model (GMM) is proposed to deal with the multi-criteria ABC inventory classification problem. GMM is a simple optimization model that can be used for classification purposes with a low computational time. A numerical investigation of the cost-service inventory of the GMM model is presented in this paper. The performance of the model is also compared to some mathematical programming-based MCIC models. The numerical study is conducted by means of a theoretical dataset, consisting of 47 stock keeping units, which has been commonly used in the literature. The numerical results show that the proposed model can have a promised performance along with the existing MCIC classification models in the literature in terms of the cost-service inventory efficiency.

In this paper a “state-space” revised version of the consecutive compensator control approach is proposed to regulate systems with uncertainties and input constraints affected by external disturbances. As a preliminary result the redesigned controller is applied to a system coupled with unmodeled dynamics. The main result enhances this approach by implementing the internal model and back-calculation anti-windup scheme in the control law. The paper throughly addresses the stability issues and provides a numerical example illustrating the proposed approach.

This paper describes development of High speed current sensing system (HSCSS) for measurement of current and voltage during Cold Metal Transfer welding (CMT). The main purpose of this system is to detect faults during welding and mark damaged joints before product left welding area. HSCSS consist of sensor board based on STM32F7 processor with additional sensors and evaluation software running on remote PC. This paper is focused on firmware of the sensor board.

Data streams are collected from the real time operation of complex machines, plants and other technological systems. The collected information could be further used for different purposes, such as performance evaluation, anomaly detection, change detection or fault diagnosis of the operating systems. The analysis of the data streams is done by a calculation tool for estimating the similarity level between pairs of given chunks of data from the data stream, considered as data clouds. One of these data cloud represents a prerecorded (previously known) abnormal operation of the system, while the other data cloud represents a current (still unknown) behavior of the system. Then the similarity analysis will show how close the two data clouds are. In this paper we propose a novel method for similarity analysis that uses two types of models called Data Cloud Model (DCM) and Window Cloud Model (WCM). The DCM is obtained from the data cloud that represents a previously known operation of the system, while the WCM is obtained from the newly collected data cloud from the data stream, called window data cloud. Both data clouds have an equal length (number of data). The algorithms for creating the DCM and WCM are explained in the paper. The DCM consists of Active Grid cells that represent approximately the data density within the known data cloud. The WCM estimates the data density at the same active grid cells, but based on the data from the new window data cloud. Both densities are represented as two Histograms that are compared to each other in order to calculate the similarity level as a value between 0.0 and 1.0. Another problem discussed in the paper is finding a plausible method for detection of significant changes in the data streams. Here the moving window technique is used for collecting series of subsequent data clouds. Then two procedures of moving windows are run in parallel, each of them with different window lengths, in order to calculate the center-of-gravity of the respective data in a real time. The difference between the results of the two moving windows is used as an estimate of the change in the process operation. Both technologies developed in this paper are explain in details in the paper and illustrated on the example of real data stream from a petrochemical plant.

In this paper, we investigate computational methods for synthesizing optimal control inputs for nonholonomic control systems. We use the Brockett integrator as a benchmark example to illustrate different aspects of the computational trajectory optimization problem. The main result of this paper is the establishment of a rather attractive iterative scheme for practically constructing optimal control inputs. The functionality and efficiency of the proposed iterative scheme are illustrated on different optimal steering problems centered around the Brockett integrator.

It is often assumed that data used to plan operations and supply chain activities is accurate. But in the presence of uncertainty, this assumption is known not to be entirely true. In this context, it becomes relevant to evaluate if a planning decision is appropriate in light of partially accurate data. This paper proposes a general framework for data analysis in order to provide a quality evaluation of the information used in the decision-making process. To this end we propose a process to quantify data quality by comparing “measured” data to “real” data. We use a hybrid approach combining multiple data quality assessment techniques as well as different alternative sources of historic data. A classification phase then rates and «tags» data for proper consideration for decision-making. Such classification provides insights into the level of uncertainty associated with the data. This paper demonstrates the approach developed using a case study from the forest sector. The approach can be adapted to other industrial sectors.

This paper presents a comparison of energy management strategies based on Pon-tryagin’s Minimums Principle (PMP). The fuel consumption of Plug-in-Hybrid Electric Vehicles is minimized by considering the power split and electrical driving decision as optimization variables. This paper discusses the trade-off between optimality and computational efficiency of analytical and map-based PMP methods. Thereby a decision guidance for further research is presented which evaluates the discussed methods by contrasting fuel consumption, state of charge trajectory, power split and engine on/off decision and computational efficiency.

Trained human pilots or operators stand out through their efficient, robust, and versatile spatial guidance and control skills. The hypothesis is that trained operators learn sensory-motor primitives previously described as interaction patterns, and proposed as units of behavior for organization and planning of behavior. The interaction patterns emerge as a result of repeated interactions with a task environment. This paper extends a previously presented modeling and analysis framework based on interaction patterns to evaluate human learning of unknown environments. The paper presents a hierarchical clustering method to extract interaction patterns from trajectory data over successive runs, and uses these patterns for the analysis of the perceptual and control characteristics while accounting for different skill levels.

The paper presents a set of modules developed in the Siemens Plant Simulation software, which automatically creates the models of robotic assembly systems with AGV based material handling, and supports the production and maintenance planning. The layout planning module creates the layout from a predefined set of components. The area analyser and track configurator modules map the free area in the layout and create the AGV tracks. The maintenance planning module optimises the maintenance schedules of the system. The production control module creates the production schedules and executes an integrated maintenance and production simulation.

This paper discusses a possibility how to make learning of control theory basics more interesting for an individual learner. Specifically, an approach of learning by doing is applied when using a low-cost Arduino architecture for each student. As an example of dynamical systems simple RLC-circuits have been chosen. The advantage of Matlab/Simulink support of Arduino platform has enabled students to perform programming tasks from known environment. This paper concentrates on a limited part of the control theory, namely the static and dynamic characteristics. Particularly, in this paper we will present how to construct different RLC circuits, measure following characteristics: static I/O characteristic, step, impulse and frequency responses and identify their parameters.

This paper presents a methodology for determining the variability of technological operations times, which is suitable for predictive scheduling of production tasks. The paper begins with outlining the problems related to production scheduling in real production systems and the process of robust job scheduling. In the work, a special attention was paid to the uncertainties associated with the variability of the times of technological operations of individual jobs. Subsequently, the proprietary prediction algorithm for the variability of technological operations times was discussed, and the results of its operation were verified using historical production data. The work was completed with the analysis of the obtained results, as well as the outlining the direction of further work in the analysed problem area.

Thanks to the automation of diagnostic processes based on the EEG signal and the creation of classifications of various attention disorders based on the analysis of the EEG signals (as diagnostic markers) - it may be possible to refer patients to appropriate specialists and provide them with appropriate neurofeedback-based treatment. The marker method proposed by the authors of the hereof paper is based on analysis of the EEG, which signal can be widely use in screening diagnostics. The proposed solution in this work may be used for neurofeedback or Brain Computer Interfaces (BCI). In this paper the authors presented implementation of markers for the purpose of analysis of some memory disorders and short-time attention disorder. The study is still in progress. Presented results have initial-study character.

This paper describes OPC UA interoperability, OPC UA servers’ profiles, and basic concepts of address space based on OPC UA information model. It also contains a practical experiment where OPC UA server and client are implemented to fulfill following requirements for Micro Embedded Device 2017 Server Profile and integrated at microcontrollers which run a real time operating system. The experiment is focused on a signal processing speed. It contains a synthesis of the server and client design, starting with choosing a software and a hardware, through a device description by Information Model, and ending with the explanation of experiment results. The paper also discusses possible benefits which could be achieved by integrating embedded devices with OPC UA into an automation control infrastructure.

The loss of cultural diversity impacts international stability by undermining sustainable development goals. UNESCO is charged with promoting cultural diversity, guided by important, globally recognised, conventions. IFAC researchers have recently directed attention to the application of automation systems to the preservation of cultural heritage. This paper reports developments in digital cultural heritage and media automation. Using a human-centred approach, the paper presents a systems project which places culture at the centre of technical development. Leading metadata standards are surveyed and machine-readable ontological models proposed which can describe intangible and tangible cultural heritage as envisaged by UNESCO. This study proposes a digitisation process which encodes artefact properties in XML to be linked into the ontologies. It also synthesises an architecture to guide the work of a new research laboratory. This study embodies a new trans-disciplinary research agenda at the interface of systems engineering and the humanities.

Cybersecurity is a fast growing domain. The supply of workforce entering the labour market can not match the current demands. Due to this currently existing and predicted future skills gap in the labour market, educational institutions attempt to minimize dropouts and study times. As a direct consequence, the relevance of valid admission and selection procedures has grown in recent years. However, there is a mismatch between the increased demand for high-quality admission procedures and the still existing lack of established methods and routines to conduct these. In this paper we discuss our experience from running admissions in one of the oldest European master level cybersecurity curricula in Europe. We argue that cybersecurity skills assessment cannot simply be traditional knowledge-based assessments as this may exclude suitable candidates, who have not had the opportunity to learn the subject matter or are joining from different fields. Also selection decision cannot be done purely based on previous grades, because decomposing school subjects into cybersecurity skills is challenging due to the domain’s interdisciplinary nature. We present a technical skills assessment method using cloud-based virtual labs that can be done by the candidates remotely. Those labs focus on assessing the technical competencies of a candidate and leave the assessment of non-technical skills (which are at least equally important) to a human interviewer. Also identifying cheaters, who do not prepare their labs themselves, will be left for the human interviewer. Such on-line exercises show potential as scalable option to evaluate the cybersecurity technical skills, motivational levels and cognitive strategies applied for problem-solving in a complex, novel task when being under performance pressure. The lessons learned are shared; feedback obtained from the applicants and possible technical metrics for predicting their success in a cybersecurity program are explored. As further work, we plan to conduct full data analysis and time-delayed interviews to generate hypothesis that can be further empirically tested with appropriate designs to detect causal relationships.

Data-driven prediction of future events is to provide decision-makers Predictive Information (PI) to decrease human-error. They usually desire possession of a predictor which works independently from the humanized configurations and works efficiently and accurately. The accurate data-driven prediction of the systems’ behavior is the primary focus of this paper. We define the future state of a system is a set of uncertain values, which can be modeled by fuzzy numbers. The future machine state is not very dissimilar to the current status, and the next event is a sort of behavior repetition. The PI also justifies the system being in a trend to achieve a goal, and it counts the unplanned contextual reactions of the system. In this paper, we come up with a fuzzy data-driven predictor application to foretell the system behavior.

In this paper we introduce disropt, a Python package for distributed optimization over networks. We focus on cooperative set-ups in which an optimization problem must be solved by peer-to-peer processors (without central coordinators) that have access only to partial knowledge of the entire problem. To reflect this, agents in disropt are modeled as entities that are initialized with their local knowledge of the problem. Agents then run local routines and communicate with each other to solve the global optimization problem. A simple syntax has been designed to allow for an easy modeling of the problems. The package comes with many distributed optimization algorithms that are already embedded. Moreover, the package provides full-fledged functionalities for communication and local computation, which can be used to design and implement new algorithms. disropt is available at github.com/disropt/disropt under the GPL license, with a complete documentation and many examples.

Future aircraft tend to have increased flexibility that leads to increased aeroservoe-lastic (ASE) effects. Therefore, future aircraft control systems need active control to suppress ASE effects. Active flutter suppression can be effectively done in the linear parameter varying (LPV) framework. Control surface sizing for aircraft is traditionally done by iterations. In this approach engineering rules are used to determine the size of the control surfaces and the control laws are designed afterwards. Such method, besides being time consuming, might have further challenges in the future due to the coupling between the flexible and rigid body dynamics. Instead, ”co-design” was recently proposed. In the co-design approach parametric aircraft models are developed based on which the control surface sizing and the control design are optimized in a single step. The purpose of this paper is to create a control oriented, parametric control surface model of the mini MUTT aircraft that can be applied for co-design. The resulting control oriented LPV model needs to have sufficiently low dynamic order, which is achieved by the ”bottom-up” modeling approach. A grid based LPV model is obtained from the nonlinear model by Jacobian linearization and the Tensor Product (TP) type polytopic model is obtained from the grid based LPV model via TP model transformation. The resulting low order parametric control surface LPV models are assessed with the v-gap metric. These models can serve as the basis of simultaneous baseline/flutter suppression control synthesis and control surface sizing optimization.

Due to he complexity of the Internet of Things systems, embedded devices require complex hardware, usually composed of various microprocessors communicating with each other. This calls for deep understanding of communication protocols and features offered by various components for designers and programmers alike. Unfortunately, the available hardware platforms for prototyping IoT devices are not adapted to such complex design and do not tackle these aspects. In the paper we propose the Copernicus board, composed of two different microprocessors, focusing on the software aspects of IoT device design. The device is used in the Introduction to IoT course which is part of the Computer Science curriculum at AGH University of Science and Technology. At the end of the paper we summarize student interest in the course, in the form of statistics.

This paper describes a recently deployed concept of Laboratory of Things at our institute. It uses a laboratory network of experimental devices and processes from the field of automatic control. Paper gives a technical description of an architecture of the eLab network, hardware components such as I/O server, software, communication principles and, overall operation of the solution. The laboratory instruments are provided as network services via several different protocols and access methods that can be used in educational process with minimal effort. Students can work with laboratory equipment using their personal computers without the need of specialized interfacing hardware. Several technological processes and experimental devices are currently provided. The paper also describes connections between eLab and MATLAB.

The aim of the paper is to contribute to the area of online experiment management. Although such systems already exist, the proposed solution tries to enhance their features by providing users with an extended user interface. The paper suggests the most efficient architecture and explains the reasons for using each component. After the examination of the system interfaces and their role in the overall data flow, the paper focuses on the achieved functionality and its benefits. The attention is devoted to the modification of control algorithms by the user, running experiments in prescheduled time and also in the batch mode. The experiments are supported by various simulation environments - e.g. Matlab, SciLab, OpenModelica. However, the use of classical program language, as the C language, is also supported. What differentiates this system from others like it, is the added value of social networking enabled by several communication tools.

This paper proposes a vehicle-to-grid (V2G) charging framework has been proposed in this paper owing to the optimal charging/discharging characteristics of the electric vehicles’(EVs) batteries with respect to the change in frequency in the grid. Since battery overcharging deteriorates its lifetime, the proposed charging framework ensures virtual tripping of the vehicles from the plug-in charging terminal once it is fully charged. Additionally, a faster DC bus voltage droop controller has been used for the voltage source converter (VSC) instead of the conventional low bandwidth PI controller to overcome the sluggish response. A negative feedforward signal alongwith the droop controller has been used to achieve DC voltage regulation. On basis of extensive simulation carried out in MATLAB/SIMULINK, the results demonstrate the effective performance of the V2G charging strategy as well as the faster performance of droop controller over PI controller.

Model-Based System Engineering (MBSE) is an increasingly important methodology to support system engineering and has attained a high level of attentiveness in business simulation practices as a conceptual modelling approach. In this paper, we present our results related to the application of MBSE approaches in complex semiconductor manufacturing supply chain planning systems. We investigate System Modeling Language (SysML), Web Ontology Language (OWL) and Business Process Modeling Notation (BPMN) as different approaches and languages for MBSE. These approaches are surveyed and used to develop conceptual models for the simulation of the order management process inside the supply chain management. This study aims to survey and offer a number of implications for MBSE practice and seeks to stimulate and guide further research in this area.

Reducing the dimensionality of a fault detection and identification problem is often a necessity, and variable selection is a practical way to do it. Methods based on mutual information have been successful in that regard, but their applicability to industrial processes is limited by characteristics of the process variables such as their variability across fault occurrences. The paper introduces a new estimation strategy of mutual information criteria using alarm series to improve the robustness of the variable selection. The minimal-redundancy-maximal-relevance criterion on alarm series is suggested as new reference criterion, and the results are validated on a multiphase flow facility.

14th IFAC Conference on Programmable Devices and Embedded Systems (PDES), WOS: 000401255800072, This paper presents PID and SMC control schemes on a Field Programmable Gate Array device (FPGA) for robot manipulators. The implementation of the control systems on FPGA by using optimal hardware resources is one of the challenging research areas in control engineering. To accomplish this, MATLAB Xilinx System Generator toolbox plays an important role in control design on an FPGA device. In this paper, FPGA-based PD and SMC controllers are designed by using MATLAB Xilinx System Generator tool for the chosen robot system. The tracking performances of the presented control schemes, implemented in Matlab/Simulink and implemented on FPGA, are compared. Robustness and good trajectory performance of the system on FPGA are demonstrated. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved., Int Federat Automat Control, Tech Comm 4 1 Components & Technologies Control, Int Federat Automat Control, Tech Comm 3 1 Comp Control, Inst Elect & Elect Engineers, Czechoslovakia Sect, TUBITAK-ARRS Project [114E954], The authors gratefully acknowledge the TUBITAK-ARRS Project, Reference No 114E954.