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Natural disasters pose a tremendous risk to the reliability of distribution networks. In this paper, a novel real-time UAV routing strategy for coordination between monitoring and inspection for post-disaster restoration in distribution networks is proposed. With our proposed real-time UAV routing strategy, damages can be inspected by UAVs for post-disaster restoration. Besides, transmission lines can be monitored to find potential dangers, and road infrastructure can also be monitored to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. In addition, due to unpredictable events during restoration, the UAV routing strategy and schedule need to be updated in real time. Then, to tackle the multi-time-scale characteristic of the proposed UAV routing strategy, a two-layer computation architecture is proposed. A case study based on the distribution network in Zaltbommel, the Netherlands, illustrates the effectiveness of the proposed method compared to other approaches.

Contains fulltext : 206183.pdf (Publisher’s version ) (Open Access) Objective: The amount of collected field data from naturalistic driving studies is quickly increasing. The data are used for, among others, developing automated driving technologies (such as crash avoidance systems), studying driver interaction with such technologies, and gaining insights into the variety of scenarios in real-world traffic. Because data collection is time consuming and requires high investments and resources, questions like "Do we have enough data?", "How much more information can we gain when obtaining more data?", and "How far are we from obtaining completeness?" are highly relevant. In fact, deducing safety claims based on collected data - for example, through testing scenarios based on collected data - requires knowledge about the degree of completeness of the data used. We propose a method for quantifying the completeness of the so-called activities in a data set. This enables us to partly answer the aforementioned questions. Method: In this article, the (traffic) data are interpreted as a sequence of different so-called scenarios that can be grouped into a finite set of scenario classes. The building blocks of scenarios are the activities. For every activity, there exists a parameterization that encodes all information in the data of each recorded activity. For each type of activity, we estimate a probability density function (pdf) of the associated parameters. Our proposed method quantifies the degree of completeness of a data set using the estimated pdfs. Results: To illustrate the proposed method, 2 different case studies are presented. First, a case study with an artificial data set, of which the underlying pdfs are known, is carried out to illustrate that the proposed method correctly quantifies the completeness of the activities. Next, a case study with real-world data is performed to quantify the degree of completeness of the acquired data for which the true pdfs are unknown. Conclusion: The presented case studies illustrate that the proposed method is able to quantify the degree of completeness of a small set of field data and can be used to deduce whether sufficient data have been collected for the purpose of the field study. Future work will focus on applying the proposed method to larger data sets. The proposed method will be used to evaluate the level of completeness of the data collection on Singaporean roads, aimed at defining relevant test cases for the autonomous vehicle road approval procedure that is being developed in Singapore. 9 p.

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

In this paper a new iterative approach is proposed for the design of a combined real-time scheduling and control algorithm that can be applied to industrial systems that are described by a hybrid model with a (semi-)cyclic behavior. Traditionally scheduling and control problems are considered in a sequential way. First the scheduling problem is solved and subsequently the control problem. This may result in inconsistent solutions such that the system may not operate adequately and does not reach the desired operational targets. In our approach scheduling is done with model predictive control using a switching max-plus linear model of the discrete event part of the system. The interface with a reference generator determines whether the computed reference signal will lead to a feasible response. Furthermore, it estimates the duration of the operations in the system based on the actual state, and communicates that with the scheduler. In an iterative procedure the optimal and feasible schedule can be computed. In a case study the railway traffic on a single track is considered, showing that updating the schedule results in feasible local speed profiles for the trains and less delay in the overall system in case of a delay.

In order to investigate the stochastic features in urban traffic dynamics, we propose a Stochastic Link Flow Model (SLFM) for signalized traffic networks with demand uncertainties. In the proposed model, the link traffic state is described using four different link state modes, and the probability for each link state mode is determined based on the stochastic link states. The SLFM model is expressed as a finite mixture approximation of the link state probabilities and the dynamic link flow models for all the four link state modes. Using data from microscopic traffic simulator SUMO, we illustrate that the proposed model can provide a reliable estimation of the link traffic states, and as well as good estimations on the link state uncertainties propagating within a signalized traffic network.

Even though various frameworks exist for reasoning under uncertainty, a realistic fault diagnosis task does not fit into any of them in a straightforward way. For each framework, only part of the available data and knowledge is in the desired format. Moreover, additional criteria, like clarity of inference and computational efficiency, require trade-offs to be made. Finally, fault diagnosis is usually just a subpart of a larger process, e.g. condition-based maintenance. Consequently, the final goal of fault diagnosis is not (just) decision making, and the outcome of the diagnosis process should be a suitable input for the subsequent reasoning process. In this paper, we analyze how a knowledge-based diagnosis task is influenced by uncertainty, investigate which additional objectives are of relevance, and compare how these characteristics and objectives are handled in two well-known frameworks, namely the Bayesian and the Dempster-Shafer reasoning framework. In contrast to previous works, which take the reasoning method as the starting point, we start from the application, knowledge-based fault diagnosis, and examine the effectiveness of different reasoning methods for this specific application. It is concluded that the suitability of each reasoning method highly depends on the problem under consideration and on the requirements of the user. The best framework can only be assigned given that the problem (including uncertainty characteristics) and the user requirements are completely known.

Last-minute maintenance planning is often undesirable, as it may cause downtime during operational hours, may require rescheduling of other activities, and does not allow to optimize the management of spare parts, material, and personnel. In spite of the aforementioned drawbacks of last-minute planning, most existing methods plan maintenance activities at the last minute. In this paper, we propose a new strategy for timely maintenance planning in multi-component systems. As a first step, we determine for each system component independently the most appropriate maintenance planning strategy. This way, the maintenance decisions can be tailored to the specific situations. For example, conservative maintenance decisions can be taken when the risk tolerance is low, and maintenance decisions can be made timely when we can accurately predict future degradation behavior. In the second step, we optimize the maintenance plan at the system level. Here, we account for economic and structural dependence with the aim to profit from spreading or combining various maintenance activities. The applicability of the method is demonstrated on a railway case. It is shown how the different cost functions (e.g. costs of maintenance, downtime, and failure) influence the maintenance decisions.

Distributing water optimally is a complex problem that many farmers face yearly, especially in times of drought. In this work, we propose optimization‐based feedback control to improve crop yield and water productivity in agriculture irrigation for a plantation consisting of multiple fields. The interaction between soil, water, crop (sugarcane in this work), and the atmosphere is characterized by an agro‐hydrological model using the crop water productivity modeling software AquaCrop‐OS. To optimally distribute water over the fields, we propose a two‐level optimal control approach. In this approach, the seasonal irrigation planner determines the optimal allocation of water over the fields for the entire growth season to maximize the crop yield, by considering an approximation of the crop productivity function. In addition, the model predictive controller takes care of the daily regulation of the soil moisture, respecting the water distribution decided on by the seasonal planner. To reduce the computational complexity of the daily controller, a mixed‐logic dynamical model is identified based on the AquaCrop‐OS model. This dynamical model incorporates saturation dynamics explicitly to improve model quality. To further improve performance, we create an evapotranspiration model by considering the expected development of the crop over the season using remote‐sensing‐based measurements of the canopy cover. The performance of the two‐level approach is evaluated through a closed‐loop simulation in AquaCrop‐OS of a real sugarcane plantation in Mozambique. Our optimal control approach boosts water productivity by up to 30% compared to local heuristics and can respect water use constraints that arise in times of drought.

Adequate fault diagnosis requires actual system data to discriminate between healthy behavior and various types of faulty behavior. Especially in large networks, it is often impracticable to monitor a large number of variables for each subsystem. This results in a need for fault diagnosis methods that can work with a limited set of monitoring signals. In this paper, we propose such an approach for fault diagnosis in networks. This approach is knowledge-based and uses the temporal, spatial, and spatio-temporal network dependencies as diagnostic features. These features can be derived from the existing monitoring signals; so no additional sensors are required. Besides that the proposed approach requires only a few monitoring devices, it is, thanks to the use of the spatial dependencies, robust with respect to environmental disturbances. For a railway track circuit example, we show that, without the temporal, spatial, and spatio-temporal features, it is not possible to identify the cause of a detected fault. Including the additional features allows potential causes to be identified. For the track circuit case, based on one signal, we can distinguish between six fault classes.

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.

In this paper a new multi-class urban traffic model is proposed based on the features of a single-class urban traffic model and the characteristics of a multi-class freeway traffic model. The heterogeneous traffic flow is represented using the concept of Passenger Car Equivalent (PCE) for congestion and free-flow regimes separately. The proposed multi-class urban traffic model is intended for model-based control applications. The single-class model and the proposed multi-class traffic model are compared with microscopic simulation data obtained using the SUMO (Simulation of Urban MObility) open-source simulator. The two models are calibrated through optimal parameter estimation and their performance is evaluated and compared by taking into account the error index between the models and the simulation data. Simulation results show that the multi-class model gives a significantly better fit.

An integrated model for line planning and train scheduling based on the circulation of trains is proposed to reduce passenger dissatisfaction and operation costs for an urban rail transit line. The turnaround operations of trains and their departures from and arrivals at the depot were included in this model. Furthermore, binary variables were introduced to indicate whether train service existed, and a discrete event model was used to determine the order of the train services. In addition, a bi-level optimization approach is proposed to solve the integrated line-planning and train-scheduling problem, in which the number of required train services, the headways between train services, the departure times, and the arrival times were optimized simultaneously on the basis of passenger demand. The performance of the proposed integrated model and bi-level approach is illustrated with a case study of the Beijing Yizhuang line.

We use the concept of a Map of Interfacial Periodicity to visualize and classify the periodicity at hetero-phase boundaries. Periodicity in the plane of the interface is a necessary condition to achieve an optimized bonding arrangement across the interface. A periodic boundary plane may be achieved by plane matching, i.e., a 2D match of crystal planes within the plane of the interface, or plane alignment, i.e., matching of lattice planes that are aligned across the interface. The latter mechanism results in axiotaxy and aligned epitaxy and improves robustness for the ‘matching’ nature of the interface with respect to perturbations in grain orientation or interfacial roughness. Examples are presented for different types of epitaxial interfaces between tetragonal α -FeSi 2 or orthorhombic NiSi and Si(001), two systems that are known to exhibit both axiotaxial and epitaxial texture components.

The average speed of vehicles plays an important role in traffic engineering. In almost any model-based traffic monitoring, analysis, or control application the average speed is required as a measure of performance or as an input for traffic models used to simulate fuel consumption, vehicle emissions, or traffic noise. The average speed is also used in algorithms that estimate the travel time. It also appears in the fundamental equation of traffic where density is calculated based on the average speed and flow. This article presents a new methodology for estimating the time–space–mean speed (TSMS), which is an equivalent for the generalised speed introduced by Edie [1963. “Discussion of Traffic Stream Measurements and Definitions”. Proceedings of the 2nd international symposium on the theory of traffic flow. Paris, France, 139–154]. To this aim, first tight upper and lower bounds are developed for the TSMS using individual vehicle speeds that are obtained via point measurements. To estimate the TSMS from ...

Nowadays, wind power plants (WPPs) should be able to dynamically change their power output to meet the power demanded by the transmission system operators. When the wind power generation exceeds the power demand, the WPP works in de-loading operation keeping some power reserve to be delivered into the grid to balance the frequency drop. This paper proposes to cast a model predictive control strategy as a multi-objective optimization problem which regulates the power set-points among the turbines in order to track the power demand profile, to maximize the power reserve, as well as to minimize the power losses in the inter-arrays connecting the wind turbines within the wind farm collection grid. The performance of the proposed control approach was evaluated for a wind farm of 12 turbines using a wind farm simulator to model the dynamic behavior of the wake propagation through the wind farm., This work has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sk lodowska-Curie grant agreement No 675318 (INCITE).

This paper addresses reasoning under uncertainty for knowledge-based fault diagnosis. We illustrate how the fault diagnosis task is influenced by uncertainty. Furthermore, we compare how the diagnosis task is solved in the Bayesian and the Dempster-Shafer reasoning framework, in terms of both diagnostic performance and additional objectives, like transparency, adaptability, and computational efficiency. Since the diagnosis problem is influenced by different kinds of uncertainty, it is not straightforward to determine the optimal reasoning method. First, the different uncertain influences all have their own characteristics, asking for different reasoning approaches. So, to solve the whole problem in one reasoning framework, approximations and trade-offs need to be made. Second, which types of uncertainty are present and to what extent, is highly application-specific. Therefore, the best framework can only be assigned after the problem, the uncertainty characteristics, and the user requirements are known.

SUMMARY In this paper, we analyze and compare five distributed model predictive control (DMPC) schemes using a hydro-power plant benchmark. Besides being one of the most important sources of renewable power, hydro-power plants present very interesting control challenges. The operation of a hydro-power valley involves the coordination of several subsystems over a large geographical area in order to produce the demanded energy while satisfying constraints on water levels and flows. In particular, we test the different DMPC algorithms using a 24-h power tracking scenario in which the hydro-power plant is simulated with an accurate nonlinear model. In this way, it is possible to provide qualitative and quantitative comparisons between different DMPC schemes implemented on a common benchmark, which is a type of assessment rare in the literature. Copyright © 2014 John Wiley & Sons, Ltd.

We introduce a hierachical controller, the purpose of which is to speed up the water delivery process as compared to the standard method applied currently in the field. The lower layer of the hierarchical control consists of local proportional integral filter controllers (PIF controllers) for upstream control at each gate; specifically they are proportional integral controllers with a low-pass filter. In contrast, the higher layer is composed of a centralized model-based predictive controller, which acts by controlling the head gate and by coordinating the local PIF controllers by modifying their setpoints when needed. The centralized controller is event-driven and is invoked only when there is a need for it (a water delivery request) and as such it contributes scarcely to the communication burden. The scheme is robust to temporary communication losses as the local PIF controllers are fully able to control the canal in their normal independent automatic upstream control mode until the communication links are restored. We discuss the application of the hierarchical controller to a precise numerical model of the Central California Irrigation District Main Canal. This shows the improved performance of the new hierarchical controller over the standard control method.

In this study, we focus on phase formation in intermixed Ni-Ge thin films as they represent a simplified model of the small intermixed interface layer that is believed to form upon deposition of Ni on Ge and where initial phase formation happens. A combinatorial sputter deposition technique was used to co-deposit a range of intermixed Ni-Ge thin films with Ge concentrations varying between 0 and 50 at.%Ge in a single deposition on both Ge (100) and inert SiO2 substrates. In situ X-ray diffraction and transmission electron microscopy where used to study phase formation. In almost the entire composition range under investigation, crystalline phases where found to be present in the as-deposited films. Between 36 and 48 at.%Ge, high-temperature hexagonal nickel germanides were found to occur metastabily below 300 °C, both on SiO 2 and Ge (100) substrates. For Ge concentrations in the range between 36 and 42 at.%, this hexagonal germanide phase was even found to be present at room temperature in the as-deposited films. The results obtained in this work could provide more insight in the phase sequence of a pure Ni film on Ge. © 2013 Elsevier B.V. All rights reserved. publisher: Elsevier articletitle: Phase formation in intermixed Ni–Ge thin films: Influence of Ge content and low-temperature nucleation of hexagonal nickel germanides journaltitle: Microelectronic Engineering articlelink: http://dx.doi.org/10.1016/j.mee.2013.09.004 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved. ispartof: MICROELECTRONIC ENGINEERING vol:120 pages:168-173 status: published

The model predictive control problem for max-plus-linear discrete-event systems generally leads to a nonlinear optimization problem, which may be hard to solve efficiently. In this paper, we propose to apply optimistic optimization to resolve this problem. The algorithm builds a tree where each selected control sequence corresponds to a node of the tree. An optimistic exploration of the tree is implemented, where the most promising control sequences are explored first. We give an example to illustrate the effectiveness of the method.

Hinterland haulage between major deep-sea ports and the cargoes’ inland origins or destinations has become an important component in modern logistic systems. Intermodal freight transport integrates the use of modalities (e.g., trucks, trains, and barges) during the freight delivery process to improve the efficiency and reliability of hinterland haulage. In this paper, intermodal freight transport is introduced and existing intermodal container (freight) transport planning approaches are presented. Next, a dynamic intermodal transport network (ITN) model developed by the authors in an earlier work is briefly recapitulated. To deal with the dynamic transport demand and the dynamic traffic conditions in the ITN, a so-called receding horizon approach is proposed to address the intermodal container flow assignment problem between deep-sea terminals and inland terminals in hinterland cargo transport. The proposed approach considers the movement of containers as a flow and makes container flow assignment decisions in a receding horizon fashion during the container transport process. At each time step of the process, behavior of the ITN is predicted by using a dynamic ITN model with load-dependent freeway transport times fed with information on the current and estimated transport demands and traffic conditions. To determine container assignments with this model, a nonlinear optimization problem is solved at each time step. Simulation studies for intermodal container flow assignments are conducted by using an all-or-nothing approach and the proposed receding horizon approach.

The use of traffic control systems can potentially improve the traffic flows on traffic networks. However, for the implementation of such control systems—both in simulation and in practice—many steps should be taken, and many choices are to be made. In this paper a list of considerations is provided for developing model-based traffic control systems in general, with a more detailed discussion on the use of model-predictive control for traffic regulation. A case study of designing a traffic controller is provided for the Dutch A12 freeway.

Traffic control approaches based on on-line optimization require fast and accurate integrated models for traffic flow, emission, and fuel consumption. In this context, one may want to integrate macroscopic traffic flow models with microscopic emission and fuel consumption models, which can result in shorter simulation times with fairly accurate estimates of the emissions and fuel consumption. In general, however, macroscopic traffic flow models and microscopic emission and fuel consumption models cannot be integrated with each other. We provide a general framework to integrate these two kinds of models. We illustrate the approach by considering the macroscopic traffic flow model METANET 1 and the microscopic emission and fuel consumption model VT-micro, 2 resulting in the so called the “VT-macro” model. Moreover, we characterize analytically the error introduced by the VT-macro model relative to the original VT-micro model. We further present an empirical analysis of the error and the computation time based on calibrated models of the Dutch A12 freeway.

This paper presents both offline and online optimization techniques for the control of a multiple high voltage direct current link power system. A frequency control scheme based on classical proportional-integral-derivative controllers is proposed and optimally tuned offline using particle swarm optimization. The performance of this scheme is compared with the performance of a centralized model predictive control (MPC) scheme, and a distributed MPC scheme that uses only local communications. The results illustrate that a significant performance improvement can be achieved using distributed MPC instead of classical control, illustrating the potential of distributed MPC for use in future power networks.

The paper proposes a multi-class control scheme for freeway traffic networks. This control scheme combines two control strategies, i.e. ramp metering and route guidance, in order to reduce the total time spent and the total emissions in a balanced way. In particular, the ramp metering and route guidance controllers are feedback predictive controllers, i.e. they compute the control actions not only on the basis of the measured system state, but also on the basis of the prediction of the system evolution, in terms of traffic conditions and traffic emissions. Another important feature of the controllers is that they have a multi-class nature: different classes of vehicles are considered and specific control actions are computed for each class. Since the controllers are based on a set of parameters that need to be tuned, the overall control framework also includes a module to properly determine the gains of the controllers. The simulation analysis reported in the paper shows the effectiveness of the proposed control framework and, in particular, the possibility of implementing control policies that are specific for each vehicle type.

In this paper, a method for the identification of distributed-parameter systems is proposed, based on finite-difference discretization on a grid in space and time. The method is suitable for the case when the partial differential equation describing the system is not known. The sensor locations are given and fixed, but not all grid points contain sensors. Per grid point, a model is constructed by means of lumped-parameter system identification, using measurements at neighboring grid points as inputs. As the resulting model might become overly complex due to the involvement of neighboring measurements along with their time lags, the Lasso method is used to select the most relevant measurements and so to simplify the model. Two examples are reported to illustrate the effectiveness of the method, a simulated two-dimensional heat conduction process and the construction of a greenhouse climate model from real measurements.

Fundamental macroscopic traffic variables (flow, density, and average speed) have been defined in two ways: classical (defined as either temporal or spatial averages) and generalized (defined as temporal-spatial averages). In the available literature, estimation of the generalized variables is still missing. This paper proposes a new efficient sequential algorithm for estimating the generalized traffic variables using point measurements. The algorithm takes into account those vehicles that stay between two consecutive measurement points for more than one sampling cycle and that are not detected during these sampling cycles. The algorithm is introduced for single-lane roads first, and is extended to multi-lane roads. For evaluation of the proposed approach, Next Generation SIMulation (NGSIM) data, which provides detailed information on trajectories of the vehicles on a segment of the interstate freeway I-80 in San Francisco, California is used. The simulation results illustrate the excellent performance of the sequential procedure compared with other approaches.

Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the system level, rather than at the level of the individual components. Nevertheless, almost no HVAC fault diagnosis methods that satisfy these requirements are described in literature. In this paper, we propose a multiple-model approach to system-level HVAC fault diagnosis that takes component interdependencies and multiple operating modes into account. For each operating mode, a distinct Bayesian network (diagnostic model) is defined at the system level. The models are constructed based on knowledge regarding component interdependencies and conservation laws, and based on historical data through the use of virtual sensors. We show that component interdependencies provide useful features for fault diagnosis. Incorporating these features results in better diagnosis results, especially when only a few monitoring signals are available. Simulations demonstrate the performance of the proposed method: faults are timely and correctly diagnosed, provided that the faults result in observable behavior.

In order to ensure safety against high sea water levels, in many low-lying countries, water levels are maintained at certain safety levels, and dikes have been built, while large control structures have been installed that can also be adjusted dynamically after they have been constructed. Currently, these control structures are often operated purely locally, without coordination of actions being taken at different locations. Automatically coordinating these actions is difficult, as open water systems are complex, hybrid dynamical systems, in the sense that continuous dynamics (e.g. the evolution of the water levels) appear mixed with discrete events (e.g. the opening or closing of barriers). In low lands, this complexity is increased further due to bi-directional water flows resulting from backwater effects and interconnectivity of flows in different parts of river deltas. In this paper, we propose a model predictive control (MPC) approach that is aimed at automatically coordinating the actions of control structures. The hybrid dynamical nature of the water system is explicitly taken into account. In order to relieve the computational complexity involved in solving the MPC problem, we propose TIO-MPC, where TIO stands for time-instant optimization. Using this approach, the original MPC optimization problem that uses both continuous and integer variables is transformed into a problem involving only continuous variables. Simulation studies of current and future situations are used to illustrate the behavior of the proposed scheme.

In this paper, a novel distributed Kalman filter (KF) algorithm along with a distributed model predictive control (MPC) scheme for large-scale multi-rate systems is proposed. The decomposed multi-rate system consists of smaller subsystems with linear dynamics that are coupled via states. These subsystems are multi-rate systems in the sense that either output measurements or input updates are not available at certain sampling times. Such systems can arise, e.g., when the number of sensors is smaller than the number of variables to be controlled, or when measurements of outputs cannot be completed simultaneously because of practical limitations. The multi-rate nature gives rise to lack of information, which will cause uncertainty in the system's performance. To circumvent this problem, we propose a distributed KF-based MPC scheme, in which multiple control and estimation agents each determine actions for their own parts of the system. Via communication, the agents can in a cooperative way take one another's actions into account. The main task of the proposed distributed KF is to compensate for the information loss due to the multi-rate nature of the systems by providing optimal estimation of the missing information. A demanding two-area power network example is used to demonstrate the effectiveness of the proposed method.

Trabajo presentado al 3rd IFAC International Conference on Intelligent Control and Automation Science (2013)., In a Hierarchical Model Predictive Control (H-MPC) framework, this paper explores suitable time-variant structures for the hierarchies of different local MPC controllers. The idea is to adapt to different operational conditions by changing the importance of the local controllers. This is done by defining the level of the hierarchy they belong to, and solving within each level the local MPC problem using the information provided by the higher levels at the current time step and the predicted information from the lower levels obtained in the previous time step. As selecting a hierarchy results in a combinatorial optimization problem, it is explicitly solved for a limited number of relevant topologies only and then the switching between topologies is defined with a multiobjective optimizer, so as to decide the best H-MPC scheme according to the expected performance. A comparison with fixed-topology H-MPC controllers is done, showing the effectiveness of the proposed approach for the power control of a hydro-power valley., Research supported by the European 7th Framework Net-work of Excellence Highly complex and networked control systems (HYCON2), the European COST Action TU1102 and COLCIENCIAS through Fondo Nacional de Financiamiento Para la Ciencia la Tecnología y la Innovación, Francisco Jose de Caldas, project Modelamiento y control de tráfico urbano en la ciudad de Medellín contract number 0941-2012. The work of C. Ocampo-Martinez has been supported by Secretaria d’Universitats i Recerca i del Departament d’Economia i Coneixement - Generalitat de Catalunya and the the EU Project EFFINET (FP7-ICT-2011-8-31855)

Silicides and germanides are compounds consisting of a metal and silicon or germanium. In the microelectronics industry, silicides are the material of choice for contacting silicon based devices (over the years, CoSi2, C54-TiSi2, and NiSi have been adopted), while germanides are considered as a top candidate for contacting future germanium based electronics. Since also strain engineering through the use of Si1−xGex in the source/drain/gate regions of MOSFET devices is an important technique for improving device characteristics in modern Si-based microelectronics industry, a profound understanding of the formation of silicide/germanide contacts to silicon and germanium is of utmost importance. The crystallographic texture of these films, which is defined as the statistical distribution of the orientation of the grains in the film, has been the subject of scientific studies since the 1970s. Different types of texture like epitaxy, axiotaxy, fiber, or combinations thereof have been observed in such films. In...

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC.

We present a freeway-traffic control strategy that continuously adapts traffic control measures to prevailing traffic conditions and features faster computation speed than conventional model-based predictive control (MPC). The control approach is based on the principles of state feedback control and MPC. Instead of computing the control input sequence, the proposed controller optimizes the parameters of control laws that parametrize the control input sequences. This way, the computational burden of the controller is substantially reduced. We demonstrate the proposed control approach on a calibrated model of part of the Dutch A12 freeway using variable speed limits and ramp-metering rate.

This paper proposes an efficient solution approach for the line balancing problem in state-of-the-art baggage handling systems that are based on destination coded vehicles that transport the bags from their origins to their destinations. First, a simple event-driven model of the process is presented. Next, this model is applied within the context of model predictive control to determine the optimal number of destination coded vehicles to be dispatched from the central depot to each loading station. The performance criterion is minimizing the overall baggage waiting time as well as the energy consumption.

This paper considers a variant of the travelling salesman problem where both energy consumption and variable travel speeds are taken into account. The problem describes the scheduling of pick-ups and deliveries of passengers with micro-ferries, where the energy consumption is dependent on the speed of the ferries. The schedule should be such that the ferries do not run out of energy during a trip, and time-window misfits, travel times, and waiting times for passengers are minimised. Scheduling of many transportation requests is made possible by including the charging of the ferries in the scheduling procedure, whereas the inclusion of embarking and disembarking times ensures that the passengers can board the ferry comfortably.

We present an integrated traffic management and control approach for automated highway systems (AHS). The AHS consist of interacting roadside controllers and intelligent vehicles that are organized in platoons with short intraplatoon distances and larger distances between platoons. All vehicles are assumed to be fully automated, i.e., throttle, braking, and steering commands are determined by an automated onboard controller. The proposed control approach is based on a hierarchical traffic control architecture for AHS, and it also takes the connection and transition between the nonautomated part of the road network and the AHS into account. In particular, we combine dynamic speed limits and lane allocation for the platoons on the AHS highways with access control for the on-ramps using ramp metering, and we propose a model-based predictive control approach to determine optimal speed limits and lane allocations, as well as optimal release times for the platoons at the on-ramps. To illustrate the potential of the proposed traffic control method, we apply it to a simple simulation example.

In this paper, an advanced control strategy, i.e., model predictive control (MPC), is applied to control and coordinate urban traffic networks. However, due to the nonlinearity of the prediction model, the optimization of MPC is a nonlinear nonconvex optimization problem. In this case, the online computational complexity becomes a big challenge for the MPC controller if it is implemented in a real-life traffic network. To overcome this problem, the online optimization problem is reformulated into a mixed-integer linear programming (MILP) optimization problem to increase the real-time feasibility of the MPC control strategy. The new optimization problem can be very efficiently solved by existing MILP solvers, and the global optimum of the problem is guaranteed. Moreover, we propose an approach to reduce the complexity of the MILP optimization problem even further. The simulation results show that the MILP-based MPC controllers can reach the same performance, but the time taken to solve the optimization becomes only a few seconds, which is a significant reduction, compared with the time required by the original MPC controller.

Many complex physical systems are the interconnection of lower-dimensional subsystems. For such systems, distributed stability analysis and observer design presents several advantages with respect to centralized approaches, such as modularity, easier analysis and design, and reduced computational complexity. Applications include distributed process control, traffic and communication networks, and economic systems. In this paper, we propose sequential stability analysis and observer design for distributed systems where the subsystems are represented by Takagi-Sugeno (TS) fuzzy models. The analysis and design are done sequentially for the subsystems, allowing for the online addition of new subsystems. The conditions are formulated as LMIs and are therefore easy to solve. The approach is illustrated on simulation examples.

Recently, there has been a renewed interest in the development of distributed model predictive control (MPC) techniques capable of inheriting the properties of centralized predictive controllers, such as constraint satisfaction, optimal control, closed-loop stability, etc. The objective of this paper is to design and implement in a four-tank process several distributed control algorithms that are under investigation in the research groups of the authors within the European project HD-MPC. The tested controllers are centralized and decentralized model predictive controllers schemes for tracking and several distributed MPC schemes based on (i) cooperative game theory, (ii) sensivity-based coordination mechanisms, (iii) bargaining game theory, and (iv) serial decomposition of the centralized problem. In order to analyze the controllers, a control test is proposed and a number of performance indices are defined. The experimental results of the benchmark provide an overview of the performance and the properties of several state-of-the-art distributed predictive controllers.

We investigated the phase formation and texture of nickel silicides formed during the reaction of 10nm sputter deposited nickel with Si"1"-"xC"x epitaxial layers on Si(100) substrates, having a carbon content between 0 and 2.5 atomic percent. It was found that both the formation temperature as well as the texture of the metal-rich phases is influenced by the amount of carbon in the Si"1"-"xC"x layer. To determine the influence of the location of the carbon during the silicidation process we also investigated the reaction of 10nm nickel on Si(100) substrates, where carbon was either alloyed in the nickel layer or deposited as an interlayer at the interface between the nickel and the substrate. Depending on the location of the carbon, a different thermal stability of the layer was found.

In this paper a model-based traffic control is used to design variable speed limits and on-ramp metering rates in order to reduce road traffic generated area-wide emissions near freeways. First an area-wide emission model is proposed and next a nonlinear model predictive control (MPC) approach is applied. The objectives of the MPC controller considered are the emissions, dispersions of emissions in a public area near a freeway, travel times, or the combination of these performance indicators. We compare different controlled scenarios with respect to the uncontrolled case and with respect to each other. The simulation-based case studies indicate that balanced solutions can be obtained using the proposed nonlinear MPC control strategy.

Distributed systems consist of interconnected, lower-dimensional subsystems. For such systems, distributed analysis and design present several advantages, such as modularity, easier analysis, and reduced computational complexity. Applications include distributed process control, traffic and communication networks, irrigation systems, hydropower valleys, etc. A special case of distributed systems is when the subsystems are connected in a string. By exploiting such a structure, in this paper, we propose conditions for the distributed stability analysis of Takagi-Sugeno fuzzy systems connected in a string. These conditions are extended to observer design. Sufficient LMI conditions, which are easy to solve are also provided. The approach is illustrated on a simulation example.

A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models, with linear or affine consequents. However, in practical applications, the process under consideration may be affected by unknown inputs, such as disturbances, faults or unmodeled dynamics. In this paper, we consider the problem of simultaneously estimating the state and unknown inputs in TS systems. The inputs considered in this paper are (1) polynomials in time (such as a bias in the model or an unknown ramp input acting on the model) and (2) unmodeled dynamics. The proposed observer is designed based on the known part of the fuzzy model. Conditions on the asymptotic convergence of the observer are presented and the design guarantees an ultimate bound on the error signal. The results are illustrated on a simulation example.

State-of-the-art baggage handling systems transport luggage in an automated way using destination coded vehicles (DCVs). These vehicles transport the bags at high speeds on a network of tracks. In this paper we consider the problem of controlling the route of each DCV in the system. In general this results in a nonlinear, nonconvex, mixed integer optimization problem which is usually very expensive in terms of computational effort. Therefore, we present an alternative approach for reducing the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed integer linear programming (MILP) problem. The advantage is that for MILP problems solvers are available that allow us to efficiently compute the global optimal solution. The solution of the MILP problem can then be used as a good initial starting point for the original nonlinear optimization problem. We use model predictive control (MPC) for solving the route choice problem. We also assess the performance of the proposed (nonlinear and MILP) formulations of the MPC optimization problem using a benchmark case study.

The design of a higher-layer controller using model predictive control (MPC) is considered. The higher-layer controller uses MPC to determine set-points for controllers in a lower control layer. In this paper the use of an object-oriented model of the system for making predictions is proposed. When employing such an object-oriented prediction model the MPC problem is a nonlinear, non-smooth optimization problem, with an objective function that is expensive to evaluate. Multi-start pattern search is proposed as approach to solving this problem, since it deals effectively with the local minima and the non-smoothness of the problem, and does not require expensive estimation of derivatives. Experiments in an emergency voltage control problem on a 9-bus dynamic power network show the superior performance of the proposed multi-start pattern-search approach when compared to a gradient-based approach.

Mail items of A4 size are called flats. In order to handle the large volumes of flats that have to be processed, state-of-the-art post sorting centers are equipped with dedicated flats sorting machines. The throughput of a flats sorting machine is crucial when dealing with a continually increasing number of items to be sorted in a certain time. But, the throughput is limited by the mechanical constraints. In order to optimize the efficiency of this sorting system, in this paper, several design changes are proposed and advanced model-based control methods such as optimal control and model predictive control are implemented. An event-based model of the flats sorting system is also determined using simulation. The considered control methods are compared for several scenarios. The results indicate that by using the proposed approaches the throughput can be increased with over 20%.

State-of-the-art baggage handling systems transport luggage in an automated way using destination coded vehicles (DCVs). These vehicles transport the bags at high speeds on a “mini” railway network. In this paper we consider the problem of controlling the route of each DCV in the system. This is a nonlinear, nonconvex, mixed integer optimization problem. Nonlinear model predictive control (MPC) for mixed integer problems is usually very expensive in terms of computational effort. Therefore, in this paper we present an alternative approach for reducing the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed integer linear programming (MILP) problem. The advantage is that for MILP optimization problems solvers are available to allow us to efficiently compute the global optimal solution. The solution of the MILP problem can then be used as a good initial starting point for the original nonlinear optimization problem. To assess the performance of the proposed formulation of the MPC optimization problem, we consider a benchmark case study, the results being compared for several scenarios.

A model predictive control (MPC) approach offers several advantages for controlling and coordinating urban traffic networks. To apply MPC in large urban traffic networks, a fast model that has a low on-line computational complexity is needed. In this paper, a simplified macroscopic urban traffic network model is proposed and tested. Compared with a previous model, the model reduces the computing time by enlarging its updating time intervals, and preserves the computational accuracy as much as possible. Simulation results show that the simplified model reduces the computing time significantly, compared with the previous model that provided a good trade-off between accuracy and computational complexity. We also illustrate that the simplifications introduced in the simplified model have a limited impact on the accuracy of the simulation results. As a consequence, the simplified model can be used as prediction model for MPC for larger urban traffic network.