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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 describes the dynamic modelling of a vertical take-off and landing (VTOL) aircraft and shows the flatness of the proposed model. Flat systems have the property that the inputs and the states can be written as functions of a set of the system outputs (called flat outputs) and the derivatives of these flat outputs. The flatness property allows to compute an inverse dynamic model of the given system. This can be used in path planning, nonlinear control and fault detection and isolation. The convertible aircraft presented in this paper uses redundant actuators. The advantage of this design is twofold. Firstly, it is possible to configure actuators to optimize both stationary and fast horizontal flight. Secondly, in case of one actuator failure, it provides sufficient flexibility to reconfigure the actuators in order to land safely. An important contribution of this paper is the demonstration of the flatness property for the proposed dynamical model of the convertible aircraft.

For a simplified elastic drive train with nonlinear coupling torque, this paper proposes an efficient model of the coupled system model in time-discrete form. It is obtained from piecewise-exact discretization of the continuous-time system model and allows precise, efficient feed-forward control in real time. The high model accuracy is validated in a hardware-in-the-loop setting on a laboratory test rig for elastomer couplings. The model-based control law allows safe operation of the test rig across the system’s resonance range.

Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.

Hammerstein systems are composed by a static nonlinearity followed by a linear dynamic system. The proposed method for identifying Hammerstein systems consists of a formulation within the Least Squares Support Vector Machines (LS-SVM) framework where the Impulse Response of the system is incorporated as a constraint. A fundamental aspect of this work is that the structure of the Hammerstein system allows to obtain an impulse response that approximates the linear block while LS-SVM models the nonlinearity. When the resulting model is trained, the regularization capabilities of LS-SVM are applied to the whole model. One of the main advantages of this method comes from the fact that while it incorporates information about the structure of the system, the solution of the model still follows from a simple linear system of equations. The performance of the proposed methodology is shown through two simulation examples and for different hyper-parameter tuning techniques.

This paper applies the recently developed framework for integral control on nonlinear spaces to two non-standard cases. First, we show that perfect target stabilization in presence of actuation bias holds also if this bias is state dependent. This might not be surprising, but in practice it provides an easy way to robustly cancel nonlinear dynamics of the uncontrolled plant. We specifically illustrate this for robust stabilization of a pendulum at arbitrary angle. Second, as previous work has been restricted to systems with as many control inputs as configuration dimensions, we here provide results for integral control of a non-holonomic system. More precisely, we design robust steering control of a rigid body under velocity bias.

In this short paper an original technique for deploying long coilable booms is reported. The technique consists in mechanically coupling consecutive layers, thus preventing them from uncoiling or sliding in respect to one another by using a soft polymer/adhesive applied on the annular surfaces of the coil or between layers. The polymer/adhesive not only can replace the compressing mechanism, which is usually used, which could be cumbersome and has to be manufactured, but also can eliminate the blossoming effect of the coil especially affecting long booms. The concept is validated through experiments which have shown that by using the proposed technique, deployment of long booms is possible without any compression mechanism at all, greatly simplifying the system. Moreover, the experiments have shown that it is also possible to design a system in which the polymer used as adhesive remains bonded to the main spindle and separates from the booms after deployment, which could be very useful for certain satellite applications where the booms can be used as electrodynamic tethers in which case it is preferable that their surface remains "clean" after deployment.

This paper considers the problem of optimal actuator dimensioning for LTI systems, in the sense of choosing appropriate saturation limits for a given set of admissible initial conditions and for a predefined integral state-feedback control law. By using an invariant ellipsoid argument, it is shown that this problem can be described as a linear matrix inequality (LMI)-based optimization that can be solved efficiently. Moreover, the paper shows that the optimal actuator dimensioning is connected to the choice of the initial conditions of the integral states of the controller, which can be included in the overall optimization to improve further the results. Two different methods are described and analyzed by means of simulation results. This work has been supported by the Spanish State Research Agency (AEI)through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656)and the grant Juan de la Cierva-Formacion (FJCI-2016-29019), by the DAADfunding programme: Research Grants - Short-Term Grants, 2019 (57440917),personal reference: 91725219, and by the University of Stavanger through theproject IN-12267.

In office buildings valuable energy is wasted if not properly regulated as a function of presence of humans and active demands for illumination levels. Effective and clever usage of the sunlight is essential for optimal use of energy resources in large office buildings. Additionally, productivity of the employees can be improved by maintaining a constant light intensity. In context of social distancing enforced onto landscape area structure and occupancy, they have effects in the illumination pattern and ergonomics. This paper presents the practical setup to mimic the illumination regulatory problem in landscape offices and the dynamic properties of such a system in the context of social distancing regulations. The light level control is performed with distributed predictive control, whereas a comparison is made among various situations. The original contribution of the paper is a fast, adaptive control algorithm, which can deal with changing context parameters; e.g. varying landscape office structures. Copyright (C) 2020 The Authors.

This paper discusses some issues related with the design of a bank of interval observers for uncertain switched systems, in which several sources of uncertainty are considered: parametric uncertainties, unknown disturbances, measurement noise, and unknown switching signal. More specifically, this paper focuses on analyzing the interval estimation accuracy when changes of active mode induce non-positivity of the interval state estimation errors. In particular, it is shown that by combining two types of interval observers, referred to as local and global, the accuracy and reliability of the estimation can be improved. The properties of the obtained so-called glocal observer are investigated and illustrated by means of numerical simulations. This work has been supported by the Spanish State Research Agency (AEI)through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656) andthe grant Juan de la Cierva-Formacion (FJCI-2016-29019), by the ResearchCouncil of Norway through the AMOS Center, grant number 223254 and bythe University of Stavanger through the project IN-12267

This paper addresses the so-called integrated process planning and scheduling (IPPS) problem in a reconfigurable manufacturing environment. Process planning and scheduling are two important and complex functions in manufacturing. To reduce the problem complexity, they are considered sequentially by traditional approaches. In this paper, we consider the simultaneous integration of both functions by developing a heuristic approach to solve the IPPS problem in a reconfigurable environment. Reconfigurable manufacturing systems (RMSs) comprise of a set of machines distinguished by multiple working configurations and tools. Each machine can perform a certain number of operations based on its configurations and their availability. The purpose of the proposed heuristic approach is to find the best assignment of operations to machines while considering process-quality. Finally, to demonstrate the approach applicability, an illustrative numerical example is presented and the results discussed.

Frequency-domain system representations offer important system analysis, control design and simulation tools. While this representation concept is well developed for linear time-invariant systems, there has been no general theory established for the linear parameter-varying (LPV) system class. This paper overviews and compares the existing concepts of frozen and instantaneous frequency-domain LPV representations, pointing out the need for a representation form where the effect of the spectrum of a varying scheduling trajectory can be clearly understood and analyzed on the output spectrum. For this purpose, the harmonic frequency response function (hFRF), known from the linear time-varying literature, in introduced for LPV systems. More specifically the paper demonstrates how the hFRF can be computed directly in the frequency domain for discrete-time affine input-output LPV systems.

This paper deals with the problem of the selective maintenance (SM) optimization for a series-parallel system. The system performs several missions with breaks between consecutive missions. To improve the system reliability during the next mission, its components are maintained during the breaks. Current models in the SM literature usually assume that when a component is subjected to a replacement, it is done by a new one. This paper introduces a novel variant of the selective maintenance problem (SMP) where a mixture of new and reconditioned/remanufactured parts are used to carry out replacements. It has indeed been proved that remanufacturing processes can extend the life of a product returned from the field. This provides not only economic opportunities but also favours sustainable practices. Accordingly, a novel mixed integer nonlinear programming model of the SMP is developed and optimally solved. Numerical experiments show how using reconditioned spare parts impacts the SM decisions. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Many firms have adopted Lean Manufacturing as a means of increasing productivity and reducing cost, often by focusing on the implementation of Lean best practices to identify and eliminate waste. This paper explores the application of Manufacturing Execution Systems (MES) to support the waste identification and elimination process. The majority of MES vendors have now incorporated Lean functionality in their software, but a comprehensive methodology that explains how MES can be used to enable and promote Lean Manufacturing is still lacking. This paper aims at filling this gap by presenting a Lean-MES framework. The proposed framework is validated and extended through a market analysis of MES software, identifying areas for further development for MES vendors.

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. This poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. The challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big Data analytics. This paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. The work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector.

International audience; The work presented in this paper focus on robot identification and presents a method based on the use of instrumental variables (IV). When dealing with en-bloc and offline identification of robots, the instrumental matrix constructed with the inverse dynamic model (IDM) and simulated data obtained from the simulation of the direct dynamic model (DDM). In this paper, a new recursive IV approach relevant for robot identification is presented. The instrumental matrix is constructed with the IDM and the references and their derivatives which are previously filtered by the transfer function of the position closed loop. This new way of building the instrumental matrix avoids the simulation of the DDM and offers some perspectives for online identification and real-time implementation. This recursive IV method termed IDIM-RIV (Inverse Dynamic Identification Model Recursive Instrumental Variables) is experimentally validated on the two degrees-of-freedom SCARA robot. Finally, some hints for real-time implementation are provided.

This paper presents an approach to damp out the oscillatory motion of the pendulum-like hanging platform on which a robotic manipulator is mounted. To this end, moving masses were installed on top of the platform. In this paper, asymptotic stability of the platform (which implies oscillation damping) is achieved by designing reference acceleration of the moving masses properly. A main feature of this work is that we can achieve asymptotic stability of not only the platform, but also the moving masses, which may be challenging due to the under-actuation nature. The proposed scheme is validated by the simulation studies., IFAC Symposium on Robot Control (SYROCO) 2018

Localisation is a crucial part of underwater monitoring missions. European H2020 project “subCULTron” developed a hybrid underwater sensor network with static underwater sensors capable of relocating during mission execution. The relocation and deployment is done with the aid of autonomous surface vehicles also acting as reference anchors used for underwater localisation. The motivation of this paper is determining the precision of localisation in the area of deployment. In this paper, a method of modelling localisation precision for a projection-based TDoA localisation algorithm was developed. Different spatial configurations of reference anchors can greatly impact the quality of localisation. This paper focuses on determining the quality of localisation for a given spatial configuration. Sensitivity analysis to the error in timing measurements was conducted. Simulation results are compared with results of sensitivity analysis. Finally, sensitivity analysis was tested using experimental results gathered in field trials. Index Terms— localisation, localisation precision, Underwater Acoustic Sensor Network, UASN, Time Difference of Arrival, TDoA, marine robotics, multi-robot systems

The recent trends in modern industry highlight an increasing use of robots for a wide range of applications, which span from established manufacturing operations to novel tasks characterized by a close collaboration with the operators. Although human-robot collaboration allows to relieve operators of exhausting works, an effective collaboration requires a straightforward interaction to foster the use of robot assistants. This paper provides a comprehensive survey on human-robot interaction approaches and related interfaces addressed to robot programming. An overview of on-line and off-line robot programming techniques is first presented. Then, novel intuitive interaction means, such as those based on multi-modal interaction, virtual and augmented reality, are considered. The paper aims at pointing out that collaborative robotics can effectively reduce operator’s physical workload if easy to use interfaces for robot programming are provided.

This paper demonstrates possibility to use microcontroller AURIX TC277 interfaces for resolver signal processing and also for its carrier signal generation. Resolver support is integrated in Delta-Sigma analog to digital converter module. This module also contains resolver carrier signal generation. The paper also describes needed confguration and achieved results for the electrical speeds up to 80 krpm. Measured resolver position is compared with encoder position which is taken as a reference. The results from real experiments on a prepared hardware setup are also contained. The steps needed to achieve good compensation and correspondence between both sensors are detailed.

Computation of the annihilator of a linear parameter-dependent (LPV) dynamical system is an important subtask in several fields of control theory, such as fault detection design or reconfigurable control design. While for LTI systems the problem can be efficiently handled, for LPV systems is not available a single algorithm that would provide the desired annihilator. Thus alternative solutions are needed to handle the cases that might appear in applications. This paper provides new insights and additional technical details for the authors’ previously proposed inversion based technique. As an alternative, for a special class of LPV systems where the parameter variation is affine, this paper provides an extension of the LTI techniques that computes the annihilator based on the geometric framework. A comparison of the two approaches gives an opportunity to illustrate some of the intricacies of the LPV modeling.

International audience; In this paper, a neural-network based driver advisory train cruise control system is considered. The controller assists the train driver with advisory signals by considering train and driver's actual state information (attention and fatigue) measured by dedicated sensors. Considering delays in sensor measurements, this paper aims to assess closed-loop stability of driver-in-the-loop advisory train cruise control. For this purpose, the driver model is considered as a time-varying system, the train model includes rolling and aerodynamic resistance forces and the advisory control is considered to be a sampled-data based three layer multi-layer perceptron. Further, the aperiodic measurement problem is approached as stability analysis of time-varying delayed system. Based on recent developments on the design of augemented Lyapunov Krasovskii Functional (LKF) using Bessel-Legendre inequality for time-varying delays, sufficiency conditions for the existence of L2 stability of the driver-train system in terms of solvable Linear Matrix Inequalities are provided. Further a case study is presented to illustrate the effectiveness of the proposed method.

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 a box expanding method to reduce the economic dispatch (ED) cost in the box-based robust unit commitment model (BUC). As a non-anticipative robust unit commitment model for a power system under demand uncertainty, BUC co-optimizes the commitment schedule and the feasible set of the ED problem to minimize the total operating cost for the worst-case realization in a set of possible demand scenarios; the feasible set of the ED problem is modeled as a box to enable the non-anticipative real-time dispatch. Meanwhile, as BUC considers the worst-case total operating cost, the actual total operating cost may be unnecessarily high. In this paper, the box feasible set of the ED problem in BUC is expanded to a larger one via multi-objective optimization with a no-preference method. The expanded box forms a new feasible set of the ED problem, which increases the chance of reducing the actual ED cost and thus the actual total operating cost. Simulation results using 5-, 14-, and 30-bus test systems demonstrate the effectiveness and generality of the proposed method.

Spacecraft remote sensing applications may require slew maneuvers that prioritize small ground track errors during imaging, low power consumption and quick settling time. This paper investigates attitude control with a time-varying reference for a spacecraft model actuated by reaction wheels and magnetorquers, showing (a) an analytical solution for obtaining the required reaction wheel momentum reference in a rotational maneuver; and (b) the conditions for asymptotic convergence of attitude and angular rate tracking using a quaternion-based nonlinear control law; and (c) simulation results for a 6U CubeSat in Low-Earth-Orbit performing fixed-vector pointing and slew maneuvers. In particular, if a remote sensing spacecraft shall execute a short slew maneuver and the collection of data is not required to follow a fixed ground track, then utilizing the reference quaternion propagated from initial condition may be preferred. Based on the simulated single-axis slew maneuvers, better attitude tracking performance may be achieved when the magnetorquers are actively managing the reaction wheel momentum, but decreasing their effects in the transient period may result in quicker settling time depending on chosen error tolerances.

The usage of unmanned aerial vehicles (UAVs) varies from construction inspection to disaster response. Cable attachment benefits the UAV with longer operation time and larger data transmission, while the tension, inertia, and contact forces of the cable work to disturb its operation. This paper introduces a numerical modelling method of a cable with frictional contact forces using an absolute nodal coordinate formulation (ANCF), and extracts and evaluates the influences of the cable on the mechanical system’s motion. The contacting cable length, that affects frictional contact forces, is derived using a catenary curve. The numerical analysis results present errors and vibrations at the connection point of the cable and the rigid body. These are the influences when the cable contacts the ground. In this paper, the compensation system, and its validity for mitigating these influences, using an unscented Kalman filter (UKF) to estimate the state of the cable, are presented. Although there was a small error in the mechanical system’s final position in relation to the target position, the numerical analysis indicated that the proposed control system stabilizes the mechanical system’s motion.

Wave energy converters (WECs) come in many different forms, from point absorbers and oscillating water columns to bulge wave devices. This paper focuses on the control of point absorber WECs, which typically have a narrow-banded frequency response and, therefore, control is well placed to improve the energy capture of such WECs. The acausal nature of the control problem means that, theoretically optimal control is almost impossible to achieve in practice; however, optimal velocity tracking (OVT) offers a simple and robust approximation to optimal control that can achieve better power capture than passive linear damping methods, albeit with necessarily higher force demands. OVT is a form of impedance matching and the magnitude of the power-take-off (PTO) force demand is often not linearly proportional to the WEC velocity, which can lead to PTO force and speed combinations far from the optimal PTO efficiency The highly nonlinear PTO force and speed to efficiency mapping can, without remedial measures, severely diminish the effectiveness of OVT techniques. In this paper, improvements to OVT are made, particularly regarding the limits on motion. In particular, a limit on acceleration is added and drift of the position when the acceleration and/or velocity are limited is prevented through the addition of a new integral term. An anti-wind up methodology to prevent controller integral wind up is also included. These additions allow OVT to be more easily applied in practice. The effect of PTO efficiency is explored, and a novel potential solution to the problem of adapting control to account for efficiency is presented. Both aspects of the work presented highlight the requirement for co-design of the WEC, PTO, and controller.

International audience; In a context of wind power production growth, it is necessary to optimize the levelized cost of energy by maximizing the profit of operating wind turbines, which is defined as the difference between gains (e.g. energy sold) and losses (e.g. operation and maintenance cost). Operation and maintenance cost is intimately related to fatigue damage. However, considering fatigue directly in an optimization needs to be carefully done because its faithful model does not fit standard forms. In this paper, it is first shown that the variance of a signal and its corresponding damage using fatigue theory are nonlinearly related. Therefore, this relationship is used to design a convex cost function approximating fatigue. Preliminary tests suggest promising results regarding the relevance of this formulation in optimizing fatigue trade-off when compared to a family of quadratic cost functions. The proposed formulation allows to directly consider economic parameters in the cost function, limiting thus the sensitivity to parameter tuning.

International audience; The problem addressed in this paper was motivated by a real optimization problem of supply chain of the hospital center of Troyes (HCT). The HCT is currently seeking to review and improve its logistics processes. The implementation of techniques and methods of operational research must provide solutions to improve the efficiency of logistics activities. In this work, the studied problem focuses on the catering component of the hospital logistics. A novel mathematical model for the production scheduling of multi-products and multi-stages food processes in hospital catering is proposed. This mathematical model has been implemented in commercial solver CPLEX and it has been tested on real instances of HCT and from the literature. The implementation results of the mathematical model proposed have proved its efficiency for the scheduling of the food production process.

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 investigates the dual lattice structures of self-bounded and self-hidden subspaces of linear time-invariant systems arising in the solution of disturbance decoupling, regulator and unknown-input observation problems. The case that we are addressing in this paper is the one where the algebraic feedthrough matrices are allowed to be nonzero. We show that, in this general case, the additional constraints that need to be taken into account for the solution of the aforementioned control/estimation problems are no longer simple subspace inclusions as in the strictly proper case. As a consequence, mathematical apparatus underpinning the structure of the dual lattices of self bounded and self hidden subspaces in this more general framework becomes more challenging and richer.

In this paper, the problem of guidance formulation for autonomous landing of unmanned aerial vehicles on stationary or moving or maneuvering ground vehicles at desired approach angles is considered. Nonlinear engagement kinematics have been used. While integrated nonlinear controllers have been developed in the literature for this purpose, the controller inputs often need modification of the existing autopilot structure, which is challenging. In order to avoid that, a higher-level guidance algorithm is designed in this paper leveraging a sliding mode control-based approach. In the presented guidance formulation, target-state-dependent singularity can be avoided in the guidance command. The effectiveness of the presented guidance law is verified with numerical simulation studies.

This paper focuses on integration of uncertain renewable energy sources along with relatively deterministic energy sources such as reformer based fuel cell and battery. The power mix scenario between these multiple renewable energy sources along with the reformer based fuel cell system coupled with an energy storage option, is envisaged to ensure undisrupted power supply and to combat the intermittent nature of these renewable sources. To demonstrate this scenario, a decision support system that provides an optimal decision of the power mix over a particular horizon is assumed to be available. A model predictive control scheme is deployed at the lower level control layer, to maintain the overall plant operation, and to make optimal decisions to choose the right combination of the available power sources both in the absence and presence of uncertainties in the renewable energy sources. During real time operation of the plant, due to the uncertainties in the contribution of power from solar and wind sources, the demand to the fuel cell as well as the charging and discharging profiles of the battery deviates from that planned in the upper level scheduling layer. The ability of the designed MPC to track the target power profile and to reliably overcome the uncertain behavior of the renewable energy sources by appropriately choosing between the reformer based fuel cell and the battery unit is demonstrated in this paper.

This paper presents the implementation of a GMVC-based WAPSS to damp the interarea modes of power systems. The choise for the GMVC to tackle this problem lies on the fact that it can be used to compensate the time delay due to the latency of the transmission system in a more natural way than other controllers. The paper shows that it is possible to improve system’s closed-loop stability since its behavior is the same as if the time delay is not regarded. Simulation results with Kundur’s System prove that a latency of 1 second at a conventional WAPSS might lead system’s power to oscillate for 50 seconds for a short-circuit at the transmission line, whereas the oscillation decreases to only 5 seconds if the GMVC-based WAPSS is implemented.

This paper revisits a truly classic publication: Bainbridge’s Ironies of Automation (1983) - but it also aims to make the point that the insights gained many years ago are today becoming more important than ever. As we all know, it is due to technological advances that automation is leading to increasingly complex systems which considerably raises the impact of the potential effects. Bainbridge’s insights originated from manufacturing processes, but they equally apply to process control in general and to vehicle control, e.g., airplanes, road vehicles or trains. This paper shows that comparable observations can be reported and suggests a human-centered approach to overcome the problems.

In this paper we study the adaptive control problem of integer order plants using fractional order adaptive laws in the controller. The study is based on a general methodology developed recently to establish boundeness and asymptotic behavior of solutions to multi-order systems (set of differential equations with different derivation orders) having multiple time-varying delays. Also it is based on recent results for fractional order systems under the perspective of the so called ”Error Models”. The method relies on vector Lyapunov-like functions and on comparison arguments. Boundedness and convergence of the solutions are theoretically analyzed and applications to fractional adaptive schemes are presented towards the end of the paper, including numerical simulations to verify the analytical results.

In this work, an open-source ROS interface based on KUKAVARPROXY for control of KUKA robots is compared to the commercial closed-source Robot Sensor Interface available from KUKA. This comparison looks at the difference in how these two approaches communicate with the KUKA robot controller, the response time and tracking delay one can expect with the different interfaces, and the difference in use cases for the two interfaces. The investigations showed that the KR16 with KRC2 has a 50 ms response time, and RSI has a 120 ms tracking delay, with negligible delay caused by the ROS communication stack. The results highlight that the commercial inferface is more reliable for feedback control tasks, but the proposed interface gives read and write access to variables on the controller during execution, and can be used for simple motion and tooling control. The work reported in this paper was based on activities within centre for research based innovation SFI Manufacturing in Norway, and is partially funded by the Research Council of Norway under contract number 237900.

Kitting is a materials supply principle that plays a vital role for performance in mixed model assembly systems. The kit preparation process, whereby component kits are created, is central when kitting is applied. Kit preparation is a form of materials handling and is associated with several ergonomic and quality related issues. Robotics holds a great potential for decreasing the need for human labour, but literature on the topic is scarce. The purpose of this paper is to identify the time efficiency potential of a dual robot application for kit preparation. To address the purpose, a mathematical model is developed that allows dual robot kit preparation to be analysed and compared with manual kit preparation. Furthermore, the model supports identification of a suitable batch size given a lead time requirement from the assembly system. A numerical example shows dual robot kit preparation to be slightly more efficient than its manual ditto for preparation of 2, 3 and 4 kit batch sizes. The paper’s makes a theoretical contribution in terms of the time efficiency model of dual robot kit preparation. This model is also useful for practitioners when evaluating the potential of dual robot arm kit preparation in their own processes.

The purpose of the paper is to introduce to the control community the brilliant but little-known part of Yakubovich’s academic heritage — the method of recursive objective inequalities. This method was successfully used by V.A.Yakubovich and his followers in pattern recognition, adaptive control and robotics. The paper deals with the last two topics. The most of surveyed results were published in Russian. A 1975 video about experiments with the first Soviet self-learning robot will be shown.

This paper establishes a methodology based on linear matrix inequalities (LMIs) to design a shifting H∞ linear parameter varying (LPV) state-feedback controller for systems affected by time-varying input saturations. By means of the shifting paradigm, the instantaneous saturation values are linked to a scheduling parameter vector. Then, the disturbance rejection is dealt with the quadratic boundedness concept and the shifting H∞ methodology. The design conditions are obtained within the LPV framework using ellipsoidal invariant sets, thus obtaining an LMI-based feasibility problem that can be solved via available solvers. Finally, the main characteristics of the proposed approach are validated by means of an illustrative example.

Robots are gaining traction in all industries, not only to replace manual labour but also to collaborate and enhance both human and robot skills and abilities. In this paper, we revisit the trajectory following control design for the WallMo Robot, which is a collaborative wall mounting robot used in the construction industry. The theoretical foundation for a model-free control strategy, handling actuator constraints was presented by Sloth and Pedersen (2017) and verified through simulations. In this paper, the research is extended to also include practical implementation considerations and experimental testing on a real WallMo robot. The implemented control strategy differs from what was presented earlier, but still exhibits tight trajectory tracking - solving the control problem in a practical setting.

Temperature monitoring for Lithium ion batteries is an important factor for its properties. Since the core temperature is difficult to measure, a thermal model is used for estimation. In this paper, an offline parameter identification procedure is applied, by which the parameters of a three state lumped thermal model can be identified. Based on the model a Luenberger observer is designed with the strength of compensating variations of the model parameters. Only the easily accessible and measurable temperature of the copper pole is fed back to the observer. The paper deals with the robustness analysis of the proposed observer in that the actual plant differs from the model considered in the observer. The capability of the approach is beneficial as an online (and computational power consuming) parameter identification is no longer required. Instead, the thermal model is obtained offline and the observer is robust against model parameter variations, e. g. ageing, so that a continuous update of the model is not required.

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.

In recent years, emotion recognition has attracted increasing interest from researchers from diverse fields. Because of their intrinsic correlation with emotions, physiological signals based emotion recognition method is not susceptible to the so-called social masking and thus more objective than traditional visual, audio or text data based methods. In particular, EEG signals are more responsive to emotion fluctuations than other peripheral physiological signals. In this paper, a 4-class EEG-based emotion classification problem is considered. Firstly the subjective data clustering is performed to identify the optimal number of emotional states. Then wavelet and nonlinear dynamics analyses are used to extract EEG features of emotions. Finally, we consider the brain areas for emotion generation and show that the use of only a small number of EEG electrodes placed on the frontal area of scalp can achieve a 4-class emotion classification accuracy of higher than 90%.

The interconnection of complex devices in network structures has been a challenging topic in the system identification research domain. This study presents the model identification of autonomous vehicles in platoon formation, which can be cast as a dynamic network. The paper presents the comparison between two network structures: (i) a vehicle-based network, which considers the interconnection between the vehicles based only on the velocity measurements, and (ii) a sensor-based network that considers the available sensor, i.e. the velocity and the relative distance measurements. The comparison is based on the difference between the identified transfer functions and the true ones, and the analysis of the identified air resistance coefficient variances. In addition, the paper presents the identifiability requirements for both network topologies. Simulation results show that for the same data set the variance of the identified parameters can be almost five times smaller if the system is represented as a sensor-based network, but some conditions to guarantee the identifiability of this network structure must be fulfilled.

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 study the induced probabilistic Boolean dynamics for dynamical quantum networks subject to sequential quantum measurements. In this part of the paper, we focus on closed networks of quits whose states evolutions are described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo random jumps and the corresponding measurement outcomes can be described by a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean networks can be explicitly represented through realification of the master equation. Next, when the open quantum dynamics is relaxing in the sense that it possesses a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of the relaxing property. Finally, we show that for quantum consensus networks as a type of non-relaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph.

This paper studies the widely applied fixed charge network ow problem (FCNFP), which is NP-hard. We approximate the FCNFP with a bilinear programming problem that is determined by a parameter ɛ. When ɛ is small enough, the optimal solution to the bilinear programming problem is the same as the optimal solution to the FCNFP. Therefore, solving the FCNFP can be transformed into solving a series of bilinear programming problems with decreasing ɛ. In this paper, these bilinear programming problems are solved by alternately solving two coupled linear programming problems. A dynamic method is proposed to update ɛ after solving one of the linear programming problems rather than solving the whole bilinear programming problem. Numerical experiments show the performance of the proposed method.

This paper presents a hierarchical control framework for a kite-based marine hydrokinetic (MHK) system that executes power-augmenting cross-current flight, along with simulation results based on a high-fidelity turbulent flow model that is representative of flow conditions in the Gulf Stream. The hierarchical controller is used to robustly regulate both the kite’s flight path and the intra-cycle spooling behavior, which is ultimately used to realize net positive energy production at a base station motor/generator system. Two configurations are examined in this paper: one in which the kite is suspended from a surface-mounted platform, and another in which the kite is deployed from the seabed. To evaluate the robustness of this control framework in a realistic ocean environment, we present simulation results whereby we superimpose low-frequency data from the Mid Atlantic Bight South Atlantic Bight Regional Ocean Modeling System and acoustic Doppler current profiler measurements with a high-frequency turbulence model, resulting in a high-fidelity 3D spatiotemporal flow field that is presented to the kite system. Based on this simulation framework, we demonstrate the effectiveness of the control system both in terms of robust flight and power generation.

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.