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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.

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.

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.

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.

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.

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.

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.

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 by using destination-coded vehicles (DCVs). These vehicles transport the bags at high speeds on a minirailway network. Currently, the networks are simple, with only a few junctions; otherwise bottlenecks would be created at the junctions. Bottlenecks make the system inefficient. More complex networks were considered. To optimize the performance of the system, centralized and decentralized control methods that could be used to route the DCVs through the track network were developed and compared. The proposed centralized control method is model predictive control (MPC). Because of the large computation effort that centralized MPC required, decentralized MPC and a fast decentralized heuristic approach were also proposed. When the decentralized approaches are implemented, each junction has its own local controller for positioning the switch going into the junction and the switch going out of the junction. To assess the advantages and disadvantages of centralized MPC, decentralized MPC, and the decentralized heuristic approach, a simple benchmark case study was studied. The considered control methods were compared for several scenarios. Centralized MPC becomes intractable when a large stream of bags must be handled, whereas decentralized MPC can still be used to solve the problem suboptimally. Moreover, the decentralized heuristic approach usually gives worse results than those obtained when using decentralized MPC but with very low computation time.

The Kalman filter provides an efficient means to estimate the state of a linear process, so that it minimizes the mean of the squared estimation error. However, for naturally distributed applications, the construction and tuning of a centralized observer may present difficulties. Therefore, we propose the decomposition of a linear process model into a cascade of simpler subsystems and the use of a Kalman filter to individually estimate the states of these subsystems. Both a theoretical comparison and simulation examples are presented. The theoretical results show that the distributed observers, except for special cases, do not minimize the overall error covariance, and the distributed observer system is therefore suboptimal. However, in practice, the performance achieved by the cascaded observers is comparable and in certain cases even better than the performance of the centralized observer. A distributed observer system also leads to increased modularity, reduced complexity, and lower computational costs.

Currently used traffic control measures, such as traffic signals, variable speed limits, ramp metering installations etc., are often not designed to influence the route choice of drivers. However, traffic control measures do influence the travel times that are experienced in the network. Since route choice is, at least for a part, based on experienced travel times, the control measures thus also influence the long-term route choice. This influence can be seen as a side-effect of the control measures, but in this paper we will investigate the possibilities to explicitly and actively use the influence of the traffic control measures to change the long-term route choice. Using basic traffic flow and route choice models we investigate how outflow and speed limit control can affect the final equilibrium turning fractions. As an example we consider a case study for a simple network with two routes and use a simple linear outflow controller, which makes the analytical investigation of the effects of the controller possible, but the results can be extended to more sophisticated control methods.

Large letters, journals, magazines, plastic wrapped mail items of A4 size are called flats. In order to be able 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. We propose to optimize the efficiency of this sorting system by making several design changes and implementing advanced model-based control methods such as optimal control and model predictive control. In this paper we determine an event-based model of the flats sorting system. The considered control methods are compared for several scenarios. Results indicate that using the proposed approaches the throughput can be increased with up to 52.62%, the computation of the optimal velocity being performed in real-time.

In this paper, traffic flow is controlled using dynamic speed limits, obtained by Model Predictive Control (MPC). MPC is a model-based approach, where the states of the system, influenced by control actions, are predicted over a certain time span. The states of the system are the mean speeds and densities on the motorway. Traffic flow models typically use space mean speeds, while measurements on motorways are often time mean speeds. Several methods for obtaining estimates of the space mean speed based on the time mean speeds are discussed, and the possible performance loss of using another mean speed than the space mean speed for model-based traffic control is investigated. The resulting controllers, using the different estimates, are evaluated for a scenario where speed limits are used to eliminate a shock wave from a motorway by comparing the achieved reduction in the total time that the vehicles spend on the motorway (TTS). The result show that the performance for the different estimation methods is comparable, and lead to an improvement of the TTS of around 14%

In this paper, a multi-class and multi-objective combined ramp metering and routing control strategy is proposed to improve performance in freeway traffic networks. The control strategy is of the multi-class type, i.e. different classes of vehicles are taken into account, so that specific control policies can be devised for each class. Moreover, ramp metering and route guidance are properly coordinated in order to reduce the total travel time and the total emissions in the freeway system in a balanced way. The controller proposed in the paper is of the predictive feedback type, i.e. the control computed at each time step depends on the measured system state and on the prediction of the system evolution, considering both a traffic and an emission model. Some simulation results are presented showing the effectiveness of the proposed control scheme.

In the paper we discuss the recently introduced Mobile Model Predictive Control (Mobile MPC) approach for an irrigation canal. Mobile MPC is a configuration of MPC that explicitly incorporates the role of the human operator traveling between the gates as ordered by a remote centralized controller. The operator provides the controller with up-to-date measurements from the locations visited and acts as the actuator as required by the remote controller. Mobile MPC provides a solution in between fully manual and fully automatic canal operation, as the first one may give poor performance and the second one might be impracticable in some situations, where it is not possible to rely on the equipment installed in the field. In the current paper we improve the performance of the original Mobile MPC approach by allowing the controller to decide the exact time instants when the operator should arrive at a specific gate and change the gate's settings as well as we include a penalty in the objective function for the controller to minimize the workload of the human operator. We show that the new approach yields enhanced performance in comparison to the previous method, and we demonstrate the benefits of the new method as opposed to the previous one in a case study.

A real-life motorway in Belgium is studied and a comparison is made between a simulation of a morning rush hour situation without control and a simulation of a morning rush hour situation with ramp metering implemented. Two types of controllers are compared: a traditional ALINEA based controller and a model predictive control based ramp metering controller. In order to evaluate the controllers in a realistic framework, the simulations presented in this paper are based on real-life traffic measurements, and constraints on the maximal allowed queue lengths at the on-ramps are imposed. The presented simulations are indicative for the reduction in the total time spent (on the studied motorway and on the on-ramps) that can be achieved by ramp metering during a typical morning rush hour.

The design of a traffic adaptive control system is as well a science as an art. Along the way compromises have to be made in order to end up with a workable system that is not only able to come up with good signal timings, but is also able to deliver them on time. In this paper we propose a taxonomy of the various traffic adaptive control algorithms based both on their underlying principles and the compromises that were made to come up with a workable, albeit less optimal system.

Max-plus-linear (MPL) systems are a class of discrete-event systems that can be described by models that are linear in the max-plus algebra. MPL systems arise in the context of e.g. manufacturing systems, telecommunication networks, railway networks, and parallel computing. We derive a receding horizon control scheme for MPL systems that guarantees a priori stability (in the sense of boundedness of the normalized state) of the closed-loop system in the unconstrained case. We also discuss the main properties of the resulting receding horizon controllers.

This paper described a prediction system called BSES (Boss Scenario Evaluation System), which can evaluate control scenarios in real time, predicting their ef-fects in terms of various measures of effectiveness. The system is case-based, i.e. it uses either synthetic or real-life examples of the effect of control scenarios under different circumstances. It determines the similarity of the current situation to different examples in the case-base using fuzzy logic, and uses agent-technology to predict the effects of the different measures for small sub-networks and to combine these predictions afterwards. The test results illustrate the workings of the system, and show that the system can provide the operator with real-time predictions. It is shown that the method is applicable to generalize the data it uses.

The model predictive control (MPC) framework, a very popular controller design method in the process industry, is extended to transfer coordination in railway systems. In fact, the proposed approach can also be used for other systems with both hard and soft synchronization constraints, such as logistic operations. The main aim of the control is to optimally recover from delays by breaking connections (at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear, nonconvex optimization problem. Computing an optimal MPC strategy using an extended linear complementarity problem is demonstrated. Also presented is an extension with an extra degree of freedom to recover from delays by letting some trains run faster than usual (again at a cost). The resulting extended MPC railway problem can also be solved using an extended linear complementarity problem.

The paper describes the evaluation studies of a learning game for older adults using a multicultural European sample. The goal of the evaluation was to explore the acquisition of knowledge as a result of game play, the acceptance of two different game interaction systems by the seniors, their experiences while playing the game, and the accessibility of the game’s user interface. Several qualitative and quantitative research methods were combined among older adults in the Netherlands, Spain and Greece. The tests revealed that (i) the accessibility of the game menus and interaction devices was high, (ii) the player experience in digital game play with a TV remote control and 3D sensor were mostly positive, and (iii) the older adults reported that they experienced learning. Overall, it can be concluded that the goals were met, offering a similar accessibility and player experience between the three different countries, and to provide a learning experience for older Europeans.

This paper proposes a multi-agent cooperative intermodal freight transport planning approach for multiple intermodal freight transport operators (IFTOs) in the control of container flows. The cooperation goal is to minimize the overall freight delivery cost for serving certain transport demands. Based on a distributed model predictive control methodology, a cooperative planning approach is proposed by decomposing the augmented Lagrangian formulation of the joint intermodal freight transport planning problem into subproblems that are then cooperatively solved by the IFTOs through iterative exchange of planning information. A simulation study on cooperative intermodal freight transport planning illustrates the potential of the proposed cooperative planning approach.

This thesis focusses on Dutch regional water systems. These systems are often low-lying polder-belt canal systems, where many pumps are needed to meet different requirements regarding water quantity and quality. The control of a complex water system, consisting of continuous dynamics (evolution of water flow and levels) and discrete elements (e.g. barriers and pumps that are operated on or off) can be optimized using a so-called hybrid Model Predictive Controller. However, particularly the optimization of the combination of discrete and continuous elements requires extensive computational effort. Even with the ongoing increase in computational power, computational time remains an issue for the optimization of large hybrid systems in real-time control applications. Time Instant Optimization MPC has been proposed in literature as an alternative to the computationally more demanding Mixed Logical Dynamical models. TIO-MPC involves the optimization of a (a priori determined) number of time instants, which are the moments that a discrete variable changes its state. The rationale behind this approach is that in many cases, it is undesirable to have too many switching of controllers. This significantly reduces the amount of optimization variables, as the controller does not have to decide at every time step whether or not to switch the state of discrete variables. The latter would normally lead to a large combinatorial optimization problem. The contribution of this thesis is the extension of the current TIO-MPC in such a way that the time instants become continuous. This way, the gradient of the objective function can be derived. The gradient allows the use of efficient gradient-based solvers, which require a gradient vector of the objective function for finding an optimum. Furthermore, hybrid schemes including the optimization of time instants and standard MPC can easily be integrated. The analytical gradient of the objective function is derived by applying algorithmic differentiation in reverse mode. This way, the gradient of the objective function can be derived to machine precision at the computational costs of a single function evaluation. Another benefit of the use of algorithmic differentiation over finite differencing is the absence of a truncation error. Multiple TIO-MPC algorithms have been designed, and their performance has been tested using two test cases. Different performance indicators are defined and used to compare the results. The first test case involves the closed-loop simulation of a fictitious linear reservoir with one discrete control variable (pump) using two time instants. The second test case involves the pump scheduling of two continuous pumps, two discrete pumps and one a gate on a model of the Fivelingo boezem, an existing water system in the province of Groningen in the north of the Netherlands. For the latter, only open-loop simulations have been carried out using algorithms with a different number of time instants. For comparison, one continuous optimization was done. The gradient-based TIO-MPC algorithms are perfectly capable of optimizing both continuous and discrete elements, well within the allowed control time step. The computational time is correlated with the amount of iterations that is needed to converge to a solution. Therefore it cannot be said in advance which scheme is the quickest. A comparison of the different TIO-MPC algorithms used for the Fivelingo test case demonstrated that the use of more time instants generally leads to lower objective function values, indicating better control performance. The experiments also showed that the solution is likely to get stuck in (suboptimal) local minima if a user-supplied initial guess of the time instants is not given. Supplying a good initial guess or using a multi-start optimization procedure will potentially overcome this problem. The latter option is more realistic if one cannot come up with a good initial guess.

The ten papers in this special section are revised and extended versions of papers presented at the 8th IEEE International Conference on Intelligent Transportation Systems (ITSC'05) held in Vienna, Austria, on September 13-16, 2005. The papers in this section are briefly summarized here.

Intermodal transport is receiving increased attention due to the increasing demand for container transport in national and international trade, concerns on the sustainable development of the economy and the environment, and the recurring road congestion problems. In this paper, we first introduce intermodal transport networks and analyze the approaches for modeling intermodal transport networks in the literature. Next, a general framework for modeling intermodal transport networks is presented. In particular, a generic intermodal transport network model is formulated for the evolution of container flows in nodes and links taking into account time dependencies. The optimal route choices for container transport over the network are determined by solving a linear programming problem. Simulation experiments illustrate the properties of the proposed model.

Transportation of water is necessary to manage the water levels in rivers to ensure minimum depths for freight, to buffer water to avoid flooding downstream during wet periods, or to save water for anticipated dry periods. This manipulation of water flows influences the speed of the water in the rivers and canals, and thereby it affects the speed and energy consumption of vessels used for transportation over water. When considering the problem of scheduling micro-ferries in a harbour, the influence of fluctuating water speeds (due to the tide or river flows) can be taken into account in the aim of providing energy-efficient schedules for pick-ups and deliveries of passengers. This paper introduces the effect of flowing water on energy-efficient scheduling, and it proposes a mixed-integer linear programming formulation for solving the problem.

Based on the Ant Colony Optimization (ACO) algorithm, we previously developed an optimization method to solve the dynamic traffic routing problem in freeway networks, called Ant Colony Routing (ACR). By using Model Predictive Control (MPC), we can iteratively apply ACR at each control step to generate a control signal - i.e. splitting rates at each node in the traffic network. Motivated by the MPC framework with ACR, we show in this paper that sequential linear programming (SLP) can be used as optimization method for solving the dynamic traffic routing problem in some specific cases, resulting a lower computation time while achieving a similar performance as the ACR algorithm.

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 recent years, it has become increasingly clear that film texture can have a profound influence on the formation and stability of silicide/germanide contacts, as it controls the type and orientation of grain boundaries (affecting diffusion and agglomeration) and the interface energy (affecting nucleation during the solid-state reaction). Furthermore, the texture also has an impact on the electrical characteristics of the contact, as the orientation and size of individual grains influences functional properties such as contact resistance and sheet resistance and will induce local variations in strain and Schottky barrier height. This review aims to give a comprehensive overview of the scientific work that has been published in the field of texture studies on thin film silicide/germanide contacts. Published by AIP Publishing.

Due to increasing environmental concerns the focus of traffic management and control is shifting towards optimizing the traffic control measures to also reduce traffic emissions and fuel consumption. In this context we propose a model-based predictive traffic control approach for the balanced reduction of travel times, emissions, and fuel consumption for freeway networks, where not only the local emissions are taken into account but also the dispersion of the emissions to various target zones near the freeways. The core of the approach is a new efficient model for describing the area-wide dispersion of the emissions that is much faster than the models we have proposed in earlier papers. We present a detailed description of the new so-called expanding grid-based model and we embed it in a model-based predictive traffic control approach using variable speed limits.

This paper has two main contributions. First, it presents a simple area-wide emission (or dispersion) model for a freeway traffic networks. The model takes the variation of the wind speed and direction into account. Second, it presents a nonlinear parameterized MPC controller for freeway traffic systems. Next, the proposed model and control approach are illustrated with a simulation-based case study. The simulation results show improved traffic performance with respect to the uncontrolled system.