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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.

Max-plus linear systems are a powerful modeling tool for many applications that involve scheduling and synchronization, such as manufacturing, traffic, and legged locomotion. In this paper we investigate how to systematically construct synchronization controllers for multiple cyclic discrete-event systems modeled in the max-plus framework. We consider that a synchronization specification is given to the control designer as a set of ordering pairs of events that need to occur for the same event counter. We introduce a synchronization controller that verifies the feasible specifications and show that unfeasible specifications are automatically detected. We present simulation results for the evolution of controlled synchronized cyclic systems.

It has been shown that max-plus linear systems are well suited for applications in synchronization and scheduling, such as the generation of train timetables, manufacturing, or traffic. In this paper we show that the same is true for multi-legged locomotion. In this framework, the max-plus eigenvalue of the system matrix represents the total cycle time, whereas the max-plus eigenvector dictates the steady-state behavior. Uniqueness of the eigenstructure also indicates uniqueness of the resulting behavior. For the particular case of legged locomotion, the movement of each leg is abstracted to two-state circuits: swing and stance (leg in flight and on the ground, respectively). The generation of a gait (a manner of walking) for a multiple legged robot is then achieved by synchronizing the multiple discrete-event cycles via the max-plus framework. By construction, different gaits and gait parameters can be safely interleaved by using different system matrices. In this paper we address both the transient and steady-state behavior for a class of gaits by presenting closed-form expressions for the max-plus eigenvalue and max-plus eigenvector of the system matrix and the coupling time. The significance of this result is in showing guaranteed robustness to perturbations and gait switching, and also a systematic methodology for synthesizing controllers that allow for legged robots to change rhythms fast., Comment: Submitted

Various applications in scheduling, such as train timetables and multi-legged locomotion, can be modeled using systems of max-plus linear equations. In this framework, the eigenvalue of the system matrix represents the total cycle time, whereas the eigenvector dictates the steady-state behavior. For a class of concurrent two-state cyclic systems, with direct application to legged locomotion, we present closed-form expressions for the max-plus eigenvalue and eigenvector of the system matrix. Additionally, we probe into the transient properties of this class of max-plus linear systems by computing the coupling time.

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.

Switching gaits in many-legged robots can present challenges due to the combinatorial nature of the gait space. In this paper we present an intrinsically safe gait switching generator that minimizes the velocity variance of all the legs in stance, allowing for smooth acceleration in legged robots. The gait switching generator is modeled as a max-plus linear discrete event system which is translated to continuous time via a reference trajectory generator.

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.

In addition to the challenge to reduce traffic jams, reduction of traffic emissions in such a way that the dispersion of the emissions to residential areas, hospitals, schools, and other neighborhoods is decreased is a problem that requires state-of-the-art traffic control and management solutions. In this paper we model the dispersion of the emissions from a freeway traffic using a grid-based approach where the variability of the speed and direction wind is considered into account. The model is developed in such a way that the computation time is less than a previously proposed point-source model, while still capturing the important dispersion dynamics so that it can be used for on-line control applications. In order to reduce the dispersion of emissions to a neighborhood we design a parameterized model predictive control (MPC) strategy to optimize variable speed limits and ramp metering rates. We illustrate the proposed dispersion model and control approach with a simulation-based case study.

In order to provide safety against high sea water levels, in many low-lying countries on the one hand dunes are maintained at a certain safety level and dikes are built, while on the other hand large control structures that can be controlled dynamically are constructed. Currently, these structures are often operated purely locally, without coordination on actions between different structures. Automatically coordinating the actions is particularly difficult, since open water systems are complex, hybrid systems, in the sense that continuous dynamics (e.g., the evolution of the water levels) are 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 different actions. Hereby, the hybrid nature is explicitly addressed. In order to reduce the computational effort required to solve the hybrid MPC problem we propose to use TIO-MPC, where TIO stands for time-instant optimization. A simulation study illustrates the potential of the proposed controller in comparison with the current setup in the Rhine-Meuse delta in The Netherlands.

In this paper we compare four methods for decentralized Kalman filtering for distributed-parameter systems, which after spatial and temporal discretization, result in large-scale linear discrete-time systems. These methods are: parallel information filter, distributed information filter, distributed Kalman filter with consensus filter, and distributed Kalman filter with weighted averaging. These filters are suitable for sensor networks, where the sensor nodes perform not only sensing and computations, but also communicate estimates among each other. We consider an application of sensor networks to a heat conduction process. The performance of the decentralized filters is evaluated and compared to the centralized Kalman filter.

In order to control urban traffic with model-based control methods, a proper traffic model is very important. This traffic control model needs to have enough descriptive power to reproduce relevant traffic phenomena, and it also has to be fast enough to be used in practice. Consequently, macroscopic urban traffic flow models are usually applied as control models. In this study, a macroscopic spatiotemporally discrete urban traffic model with a variable sampling time interval is proposed for model-based control strategies. By selecting proper sampling time intervals and sampling space distances, it allows us to balance modelling accuracy and computational complexity of the spatiotemporally discrete model. In addition, an urban traffic Courant-Friedrichs-Lewy (CFL) condition is deduced for spatiotemporally discrete urban traffic models, which is a sufficient condition to guarantee the discrete model to bear enough descriptive modelling power to reproduce necessary traffic phenomena. The model is analysed and evaluated based on the model requirements for control purposes. The simulation results are compared for the situations where the CFL condition is violated and not violated.

Traffic congestion in highway networks is one of the main issues to be addressed by today's traffic management schemes. Automation combined with the increasing market penetration of on-line communication, navigation and advanced driver assistance systems will ultimately result in intelligent vehicle highway systems (IVHS) that distribute intelligence between roadside infrastructure and vehicles and that - in particular on the longer term - are one of the most promising solutions to the traffic congestion problem. In this study, the authors present a survey on traffic management and control frameworks for IVHS. First, they give a short overview of the main currently used traffic control methods for freeways. Next, they discuss IVHS-based traffic control measures. Then, various traffic management architectures for IVHS such as PATH, Dolphin, Auto21 CDS etc. are discussed and a comparison of the various frameworks is presented. Finally, the authors sketch how existing traffic control methodologies could fit in an IVHS-based traffic control set-up. © 2011 The Institution of Engineering and Technology.

In most countries, especially in deltas, there is a long tradition in the management of water resource systems, in particular related to structural measures such as the construction of dikes and riverine/coastal hydraulic structures. We discuss how this infrastructure can be supported and managed in order to serve as non-structural measures. Therefore, we present a review about technology for the prediction and control of water resources. This covers techniques for flow and water quality forecasting including modeling and data assimilation as well as its combination with predictive controllers for managing hydraulic structures. These techniques are discussed both from a conceptual and technical perspective. Furthermore, we give an overview about ongoing and future work in this field, which is aimed at integrating the techniques from both the water prediction and the water control community.

This paper reviews different observer design methods for linear dynamic distributed-parameter systems. In such systems, the states, inputs, and outputs depend on some spatial variable. This dependence, along with additional aspects such as the boundary conditions, increase the complexity of the state estimation problem and of the design methods. The paper in particular surveys observers for first-order and second-order linear distributed-parameter systems based on their infinite-dimensional and finite-dimensional descriptions.

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, 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. To assess the performance of the proposed (nonlinear and MILP) formulations of the MPC optimization problem, we consider a benchmark case study, the results being compared for several scenarios.

Traffic control has proven an effective measure to reduce traffic congestion on freeways. In order to determine appropriate control actions, it is necessary to have information on the current state of the traffic. However, not all traffic states can be measured (such as the traffic density) and so state estimation must be applied in order to obtain state information from the available measurements. Linear state estimation methods are not directly applicable, as traffic models are in general nonlinear. In this paper we propose a nonlinear approach to state estimation that is based on a Takagi-Sugeno (TS) fuzzy model representation of the METANET traffic model. By representing the METANET traffic model as a TS fuzzy system, a structured observer design procedure can be applied, whereby the convergence of the observer is guaranteed. Simulation results are presented to illustrate the quality of the estimate.

Although traffic congestion is a pressing problem that drivers face every day, improving the traffic flow does not always create a healthy environment to the people residing in the neighborhood of the freeway. Improved traffic flow neither means efficient fuel consumption of the vehicles. Moreover, reduction of total emissions or travel times in a traffic network does not always guarantee reduction in the area-wide emission levels, because there are many other factors that affect the area-wide emissions. In particular, the direction and speed of wind are important factors that play a significant role in the area-wide emission levels. Therefore, in this paper, we systematically model the effect of wind on the area-wide emission levels and design a model-based traffic controller to reduce the dispersion of emissions. More specifically, a model predictive control (MPC) is used to integrate various variable speed limits in order to provide a balanced trade-off between the area-wide emissions and the travel times. Furthermore, we present a case study to demonstrate the proposed control approach.

The paper contributes to the derivation and analysis of accelerations in freeway traffic flow models. First, a solution based on fluid dynamics and on pure mathematical manipulations is given to express accelerations. The continuous-time acceleration is then approximated by a discrete-time equivalent. By applying continues time microscopic and macroscopic traffic flow velocity definitions, spatial and material derivatives are used to describe the continuous-time and exact changes in the velocity vector field. A forward-difference Euler method is proposed to discretized the acceleration both in time and space. For applicability purposes the use of average quantities is proposed. The finite-difference approximation by space-mean speed is shown to be consistent, and its solution is convergent to the original continuous-time form. As an alternative, the acceleration obtained from a second-order macroscopic freeway model by means of physical interpretation [1] is analyzed and found to be an appropriate discrete approximations. Comparative remarks as well as future research questions conclude the paper.

Urban water supply networks are large-scale systems that transport potable water over vast geographical areas to millions of consumers. A safe and efficient operation of these networks is crucial, as without it living in today’s cities would be impossible. To achieve an adequate operation, these networks are equipped with actuators like pumps and valves, which are used to maintain water pressures and flows within safe margins. Currently, these actuators are controlled in a decentralized way using local controllers that only use local information and that do not take into account the presence of other controllers. As a result, water supply networks regularly experience pressure drops and interruptions of water supply when there is an unexpected increase in water demand. To improve performance the actions of the local controllers should be coordinated. Implementing a centralized control scheme is not tractable due to the large-scale nature of these networks. Therefore, this paper proposes the application of a distributed control scheme for control of urban water supply networks. The scheme is based on local model predictive control (MPC) strategies and a parallel coordination scheme that implements cooperation among the local MPC controllers. A simulation study based on a part of the urban water supply network of Bogotá, the capital of Colombia, illustrates the potential of the approach.

Analysis and observer design for nonlinear systems have long been investigated, but no generally applicable methods exist as yet. A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers. However, results obtained for Takagi-Sugeno fuzzy models are in general not directly applicable to the original nonlinear system. In this paper, we investigate what conclusions can be drawn and what guarantees can be expected when an observer is designed based on an approximate fuzzy model and applied to the original nonlinear system. It is shown that in general, exponential stability of the estimation error dynamics cannot be obtained. However, the estimation error is bounded. This bound is computed based on the approximation error and the Lyapunov function used. The results are illustrated using simulation examples.

A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers and controllers. However, results obtained for Takagi-Sugeno fuzzy models are in general not directly applicable to the original nonlinear system. In this paper, we investigate what conclusions can be drawn when an observer-based controller is designed for an approximate model and then applied to the original nonlinear system. In particular, we consider the case when the scheduling vector used in the membership functions of the observer depends on the states that have to be estimated. The results are illustrated using simulation examples.

Development of mechatronic systems requires collaboration among experts from different design domains. In this paper the authors identify a set of challenges related to the design of mechatronic systems. The challenges are mostly related to integration of design and analysis tools, and automation of current design practices. Addressing these challenges enables the adoption of a concurrent development approach in which the synergetic effects that characterize mechatronic systems are taken into account during design. The main argument is that in order to deal with software development problems for complex mechatronic systems, there is a need to look at system design practices beyond concurrency, i.e., there is a need to consider the complex interdependencies among subsystems and the designers that develop them. A review on current methods and tools is carried out to identify possible solutions proposed in previous works. The purpose is not to make an extensive review, but to show that integration, from different points of view, is a major issue and that increasing the level of abstraction in the description of systems can help to overcome the integration challenges. An increased level of abstraction also forms a basis for addressing other issues in mechatronic product development, which are presented in this work. With that in mind, concepts for an integration framework are proposed. The goal of the framework is to support a multi-disciplinary design team to (almost) automatically generate and verify control software. Based on high-level architectural descriptions, the software generation and verification process can be supported by knowledge-based methods and tools. Other goals are to support communication among engineers, improve reliability of designs, increase reuse of design knowledge, and reduce development time and development costs.

Power networks [15,16,24] are one of the corner stones of our modern society. The dynamics of a power network as a whole are the result of the interactions between themillions of individual components.Conventionally, the power in power networks is generated using several large power generators. This power is then transported through the transmission and distribution network to the location where it is consumed, e.g., households and industry. Power flows are then relatively predictable, and the number of control agents is relatively low. Due to the ongoing deregulation in the power generation and distribution sector in the US and Europe, the number of players involved in the generation and distribution of power has increased R. significantly. The number of source nodes of the power distribution network is increasing even further as also large-scale industrial suppliers and small-scale individual households start to feed electricity into the network [13].

Unlike mountainous areas, lowland areas have limited potential to generate energy from water flows. Instead, for water systems in lowland areas that continuously need to pump water out of the system, the focus should be set to saving energy in the present water management. This paper gives an introduction into possibilities to manage water systems in lowland areas in a sustainable manner. An example of an energy saving method is presented that is implemented by means of model predictive control on an actual physical large pumping station in The Netherlands.

We consider a hierarchical control approach for Intelligent Vehicle Highway Systems (IVHS), in which intelligent automated vehicles organized in platoons drive to their destination, controlled by a hierarchical control framework. In this framework there are several levels of controllers starting from on-board controllers inside the vehicles, over platoon controllers, to roadside and area controllers, and finally regional and supraregional controllers. In earlier work we have already described the roadside and the area controllers. In this paper we extend these results by focusing on the higher-level regional and supraregional controllers and by describing in more detail how the interactions between the different control levels work.

We present a new class of gait generation and control algorithms based on the Switching Max-Plus modeling framework that allows for the synchronization of multiple legs of walking robots. Transitions between stance and swing phases of each leg are modeled as discrete events on a system described by max-plus-linear state equations. Different gaits and gait parameters can be interleaved by using different system matrices. Switching in max-plus-linear systems offers a powerful collection of modeling, analysis, and control tools that, in particular, allow for safe transitions between different locomotion gaits that may involve breaking/enforcing synchronization or changing the order of leg lift off events. Experimental validation of the proposed algorithms is presented by the implementation of various horse gaits on a simple quadruped robot.

In previous work we have extended the popular Model Predictive Control (MPC) design technique to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In this paper we study the steady-state behavior of these so-called max-plus-linear systems in the case of an output cost criterion. We derive improved tuning rules for the controller parameters that guarantee us a feasible and stable operation of the controller in the unconstrained case. An example shows that violation of the tuning rules may destabilize the closed-loop system.

In this chapter, the operation and optimization of integrated electricity and natural gas systems is investigated. The couplings between these different infrastructures are modeled by the use of energy hubs. These serve as interface between the energy consumers on the one hand and the energy sources and transmission lines on the other hand. In previous work, we have applied a distributed control scheme to a static three-hub benchmark system, which did not involve any dynamics. In this chapter, we propose a scheme for distributed control of energy hubs that do include dynamics. The considered dynamics are caused by storage devices present in the multi-carrier system. For optimally incorporating these storage devices in the operation of the infrastructure, their capacity constraints and dynamics have to be taken into account explicitly. Therefore, we propose a distributed Model Predictive Control (MPC) scheme for improving the operation of the multi-carrier system by taking into account predicted behavior and operational constraints. Simulations in which the proposed scheme is applied to the three-hub benchmark system illustrate the potential of the approach.

The river delta in The Netherlands consists of interconnected rivers and large water bodies. Structures, such as large sluices and pumps, are available to control the local water levels and flows. The national water board is responsible for the management of the system. Its main management objectives are: protection against overtopping of dikes due to high river flows and high sea tides, supply of water during dry periods, and navigation. The system is, due to its size, divided into several subsystems that are managed by separate regional divisions of the national water board. Due to changes in local land-use, local climate, and the need for energy savings, the currently existing control systems have to be upgraded from local manual control schemes to regional model predictive control (MPC) schemes. In principle, the national objectives for the total delta require a centralized control approach integrating all regional MPC schemes. However, such centralized control is on the one hand not feasible, due to computational limitations, and on the other hand unwanted, due to the existing regional structure of the organization of the national water board. In this chapter the application of MPC is discussed for both individual regional control and coordinated national control. Results of a local MPC scheme applied to the actual water system of the North Sea Canal/Amsterdam-Rhine Canal are presented and a framework for coordination between several distributed MPC schemes is proposed.

The optimization of combined electricity and natural gas systems is addressed in this paper. The two networks are connected via energy hubs. Using the energy hub concept, the interactions between the different infrastructures can be analyzed. A system consisting of several interconnected hubs forms a distributed power generation structure where each hub is controlled by its respective control agent. Recently, a distributed control method has been applied to such a system. The overall optimization problem including the entire system is decomposed into subproblems according to the control agents. In this paper, a parallel and serial version of that method is discussed. Simulation results are obtained through experiments on a three-hub benchmark system.

Irrigation canals transport water from water sources (such as large rivers and lakes) to water users (such as farmers). Irrigation canals are typically very large in nature, covering vast geographical areas, and involving a significant number of control actuators, such as pumps, gates, and locks. The control of such canals is aimed at guaranteeing the adequate delivery of water with minimal water spillage and with minimal control structure usage. To take into account forecasts on, e.g., water consumption and weather, model predictive control (MPC) can be used to determine which actions to take. For large-scale systems, in which different parts of the canal are owned by different parties, distributed MPC control could then be employed. Although iterative distributed MPC approaches proposed earlier in the literature may yield overall optimal performance, the amount of iterations required before achieving this performance may be large, and thus require a significant amount of time. In this paper, the structure of systems consisting of serially interconnected subsystems is exploited to obtain an efficient non-iterative, cascaded MPC scheme. Simulation studies on a 7-reach irrigation canal illustrate the performance of this non-iterative scheme in comparison with an iterative scheme.

In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based on the health state, the failure rate of the equipment can be estimated. We propose to use this predictive health model to predict the effects of different maintenance actions. The effects of maintenance actions over a future time window are evaluated by a cost function. The maintenance actions are optimized using this cost function. The proposed framework is applied in the optimization of the loading of transformers based on the thermal degradation of the paper insulation. © 2009 IEEE.

A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models with linear or affine consequents. It is well known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Therefore, several stability conditions have been developed for TS fuzzy systems. We study a special class of nonlinear dynamic systems that can be decomposed into cascaded subsystems, which are represented as TS fuzzy models. We analyze the stability of the overall TS system based on the stability of the subsystems and prove that the stability of the subsystems implies the stability of the overall system. The main benefit of this approach is that it relaxes the conditions imposed when the system is globally analyzed, thereby solving some of the feasibility problems. Another benefit is that by using this approach, the dimension of the associated linear matrix inequality (LMI) problem can be reduced. For naturally distributed applications, such as multiagent systems, the construction and tuning of a centralized observer may not be feasible. Therefore, we also extend the cascaded approach to the observer design and use fuzzy observers to individually estimate the states of these subsystems. A theoretical proof of stability and simulation examples are presented. The results show that the distributed observer achieves the same performance as the centralized one, while leading to increased modularity, reduced complexity, lower computational costs, and easier tuning. Applications of such cascaded systems include multiagent systems, distributed process control, and hierarchical large-scale systems.

With the increase in the number of distributed energy resources and the amount of intelligence in electricity infrastructures, the possibilities for minimizing costs of household energy consumption increase. Household systems are hybrid systems, in the sense that they exhibit both continuous and discrete dynamics. In this paper the mixed-logical dynamic framework is used to construct a dynamic model of a household system equipped with distributed energy resources. A model predictive controller (MPC) is then proposed that uses the mixed-logical dynamic model to control the energy flows inside the household. In simulation studies we assess the performance of the proposed controller, and we illustrate how additional profits can be obtained by increasing the decision freedom of the controller.

As more and more traffic control instruments are installed, the probability increases that conflicts will arise or that coordination opportunities are lost when various traffic control measures are applied in the same area. Traffic control instruments can thus no longer be considered separately, but have to be considered as part of a larger network. By modeling the separate measures as intelligent agents, the actions of the individual instruments can be coordinated. By coordinating neighboring traffic control instruments, it can be prevented that they impede each other and one can realize that they function better as a whole. This paper proposes a procedure through which coordination between traffic control instruments can be achieved. We also illustrate the benefits of this multi-agent coordination approach for a simulation-based case study for the N470 arterial near Delft, The Netherlands, in which the new coordination approach is combined with a recently proposed block-based look-ahead traffic-adaptive control algorithm. © 2009 IEEE.

We consider traffic management and control approaches for intelligent vehicle highway systems (IVHS), which consist of interacting intelligent vehicles and intelligent roadside controllers. The vehicles 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 on-board controller. We consider both dynamic speed limit control for the platoons in the IVHS and access control at the on-ramps using ramp metering. We propose a model-based predictive control (MPC) approach to determine appropriate speed limits and release times at the on-ramps for the platoons. The proposed approach is also applied to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning dynamic speed limits and on-ramp release times.

Reinforcement learning (RL) is a widely used paradigm for learning control. Computing exact RL solutions is generally only possible when process states and control actions take values in a small discrete set. In practice, approximate algorithms are necessary. In this paper, we propose an approximate, model-based Q-iteration algorithm that relies on a fuzzy partition of the state space, and on a discretization of the action space. Using assumptions on the continuity of the dynamics and of the reward function, we show that the resulting algorithm is consistent, i.e., that the optimal solution is obtained asymptotically as the approximation accuracy increases. An experimental study indicates that a continuous reward function is also important for a predictable improvement in performance as the approximation accuracy increases.

Swarms are characterized by the ability to generate complex behavior from the coupling of simple individuals. While the swarm approach to distributed systems of moving agents is gradually finding a way to engineering applications, a true successful demonstration of an engineered swarm is still missing. One of the reasons for this is the gap between the complexity of the swarms studied in fundamental research and the complexity needed for the application to interesting control problems. In the majority of the research on swarm intelligent systems, the moving agents in the swarm are modeled as simple reactive agents. This model comprises too little intelligence to fully exploit the potential of swarms. In this paper, a general comprehensive swarm framework is introduced and related to the established state of the art. Such a framework is novel and it is a first and important step in the development and analysis of more complex and intelligent swarms.

A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models, with linear or affine consequents. It is well-known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Stability conditions developed for TS fuzzy systems in general rely on the feasibility of an associated system of linear matrix inequalities, whose complexity may grow exponentially with the number of rules. We study distributed systems, where the subsystems are represented as TS fuzzy models. For such systems, a centralized analysis is often unfeasible. We analyze the stability of the overall TS system based on the stability of the subsystems and the strength of the interconnection terms. For naturally distributed applications, such as multi-agent systems, when adding new subsystems ldquoon-linerdquo, the construction and tuning of a centralized observer is often intractable. Therefore, we also propose a decentralized approach to observer design. Applications of such systems include distributed process control, traffic networks, and economic systems.

The design of an adaptive cruise controller for a Smart car, a type of small car, is proposed as a benchmark setup for several model predictive control methods for nonlinear and piecewise affine systems. Each of these methods has been already applied to specific case studies, different from method to method. This paper has therefore the purpose of implementing and comparing them over a common benchmark, allowing us to assess the main properties, characteristics, and strong/weak points of each method. In the simulations, a realistic model of the Smart car, including gear box and engine nonlinearities, is considered. A description of the methods to be compared is presented, and the comparison results are collected in a table. In particular, the tradeoffs between complexity and accuracy of the solution, as well as computational aspects are highlighted.

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim---either explicitly or implicitly---at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided.

In switching max-plus-linear discrete event systems we can switch between different modes of operation. In each mode the discrete event system is described by a max-plus-linear state space model with different system matrices for each mode. In randomly switching max-plus-linear systems, the switching between the modes can be both deterministic and stochastic. The switching changes the structure of the system, which allows the system to break synchronization and to change the order of events. In this paper two equivalent descriptions for randomly switching max-plus-linear systems will be discussed. Furthermore, we will show that a randomly switching max-plus-linear system can be written as a piecewise affine system or a max-min-plus-scaling system. The last transformation can be established under (rather mild) additional assumptions on the boundedness of state and input.

The process of handling baggage in an airport is time critical. Currently, the fastest way to transport the luggage is to use destination coded vehicles (DCVs). These vehicles transport the bags at high speed on a ldquominirdquo railway network. The typical issues of the baggage handling system are coordination and synchronization of the processing units, minimization of the baggage travel time, avoidance of collisions between vehicles, prevention of buffer overflows, and minimization of the transportation costs. In this paper we determine an event-based model of the baggage handling system using DCVs and we investigate the use of centralized control for this system. The proposed control methods are optimal control and model predictive control. This way the optimal route and the optimal speed profile of each vehicle are determined. The considered control methods are compared for several scenarios. Results indicate that optimal control becomes intractable when a large stream of bags has to be handled, while model predictive control can still be used to suboptimally solve the problem.

Intelligent vehicle highway systems (IVHS) consist of automated highway systems in combination with intelligent vehicles and roadside controllers. The intelligent vehicles can communicate with each other and with the roadside infrastructure. The vehicles are organized in platoons with short intraplatoon distances, and larger distances between platoons. Moreover, all vehicles are assumed to be automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. In this paper we first propose a model predictive control (MPC) approach to determine appropriate speeds for the platoons. Next, we discuss which prediction models are suited to be used as an on-line traffic prediction model in MPC for IVHS. The proposed approach is then applied to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning speeds to the platoons.

Water networks are large-scale systems, consisting of many interacting components. They are currently typically operated by local decentralized controllers which receive setpoints from human operators. We discuss how communication among the local controllers can be included and in particular propose the use of distributed model predictive control for enabling the local controllers to determine setpoints autonomously using communication and coordination. We consider the control of a particular class of water networks, viz. irrigation canals. A simulation study on a 7-reach irrigation canal illustrates the potential of the proposed approach.

As more and more traffic control instruments are installed to promote the flow in road traffic networks the probability increases that either conflicts will arise or coordination opportunities are lost when traffic control instruments are applied in the same area. By modeling the separate instruments as intelligent agents, the actions of the individual instruments can be coordinated. This paper illustrates the benefits of multi-agent coordination and proposes a procedure through which coordination between traffic control instruments can be achieved.

We provide an introduction to the max-plus algebra and explain how it can be used to model a specific class of discrete event systems with synchronization but no concurrency. Such systems are called max-plus linear discrete event systems because they can be described by a model that is ldquolinearrdquo in the max-plus algebra. We discuss some key properties of the max-plus algebra and indicate how these properties can be used to analyze the behavior of max-plus linear discrete event systems. We also briefly present some control approaches for max-plus linear discrete event systems, including model predictive control. Finally, we discuss some extensions of the max-plus algebra and of max-plus linear systems.

In this paper we extend a technique developed to design a feedback stabilizing control law for autonomous switched systems all modes of which are unstable. More specifically, we extend the switching table procedure to the class of affine switched systems, the dynamics of which either do not have an equilibrium point or, if they do, it is not common. This method is then applied to the DC-DC buck-boost converter. The design of the control law is based on dynamic programming and it results in a partition of the state space into switching look-up tables. A comparison with a Lyapunov based technique is also discussed.