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Proceedings of the IEEE International Conference on Networking, Sensing and Control, ICNSC 2011, Delft, The Netherlands, 11-13 April 2011; 01/2011
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IEEE Transactions on Intelligent Transportation Systems. 01/2011; 12:846-856.
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IEEE Transactions on Systems, Man, and Cybernetics, Part B. 01/2011; 41:196-209.
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Proceedings of the IEEE International Conference on Networking, Sensing and Control, ICNSC 2011, Delft, The Netherlands, 11-13 April 2011; 01/2011
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Fuzzy Sets and Systems. 01/2011; 171:106-107.
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01/2011; Springer., ISBN: 978-3-642-16775-1
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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011; 01/2011
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ABSTRACT: Many complex physical systems are the interconnection of lower-dimensional subsystems. For such systems, distributed stability analysis and observer design presents several advantages with respect to centralized approaches, such as modularity, easier analysis and design, and reduced computational complexity. Applications include distributed process control, traffic and communication networks, and economic systems. In this paper, we propose sequential stability analysis and observer design for distributed systems where the subsystems are represented by Takagi–Sugeno (TS) fuzzy models. The analysis and design are done sequentially for the subsystems, allowing for the online addition of new subsystems. The conditions are formulated as LMIs and are therefore easy to solve. The approach is illustrated on simulation examples.
Fuzzy Sets and Systems 01/2011; 174:1-30. · 1.76 Impact Factor
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ABSTRACT: In this paper we integrate the macroscopic traffic flow model METANET with the microscopic dynamic emission and fuel consumption model VT-Micro. We use the integrated models in the model predictive control (MPC) framework to reduce exhaust emissions, fuel consumption, and travel time using dynamic speed limit control. With simulation experiments we demonstrate the countereffects and conflicting nature of the different traffic control objectives. Our simulation results indicate that a model-based traffic control approach, particularly MPC, can be used to obtain a balanced trade-off between the conflicting traffic control objectives.
07/2010;
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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IEEE Transactions on Systems, Man, and Cybernetics, Part C. 01/2010; 40:341-351.
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ABSTRACT: A large class of nonlinear systems can be well approximated by Takagi–Sugeno (TS) fuzzy models, with linear or affine consequents. However, in practical applications, the process under consideration may be affected by unknown inputs, such as disturbances, faults or unmodeled dynamics. In this paper, we consider the problem of simultaneously estimating the state and unknown inputs in TS systems. The inputs considered in this paper are (1) polynomials in time (such as a bias in the model or an unknown ramp input acting on the model) and (2) unmodeled dynamics. The proposed observer is designed based on the known part of the fuzzy model. Conditions on the asymptotic convergence of the observer are presented and the design guarantees an ultimate bound on the error signal. The results are illustrated on a simulation example.
Fuzzy Sets and Systems 01/2010; 161:2043-2065. · 1.76 Impact Factor
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ABSTRACT: Dynamic programming (DP) is a powerful paradigm for general, nonlinear optimal control. Computing exact DP solutions is in general only possible when the process states and the control actions take values in a small discrete set. In practice, it is necessary to approximate the solutions. Therefore, we propose an algorithm for approximate DP that relies on a fuzzy partition of the state space, and on a discretization of the action space. This fuzzy Q-iteration algorithm works for deterministic processes, under the discounted return criterion. We prove that fuzzy Q-iteration asymptotically converges to a solution that lies within a bound of the optimal solution. A bound on the suboptimality of the solution obtained in a finite number of iterations is also derived. Under continuity assumptions on the dynamics and on the reward function, we show that fuzzy Q-iteration is consistent, i.e., that it asymptotically obtains the optimal solution as the approximation accuracy increases. These properties hold both when the parameters of the approximator are updated in a synchronous fashion, and when they are updated asynchronously. The asynchronous algorithm is proven to converge at least as fast as the synchronous one. The performance of fuzzy Q-iteration is illustrated in a two-link manipulator control problem.
Automatica. 01/2010; 46:804-814.
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ABSTRACT: In this paper we first present an extension of the macroscopic traffic flow model METANET to multi-class flows. The resulting multi-class model takes into account the differences between, e.g., fast vehicles (cars) and slow vehicles (trucks) including their possibly different free-flow speeds and critical densities. Next, we show how this model can be used in a model-based predictive control approach for coordinated and integrated traffic flow control. In particular, we use Model Predictive Control (MPC) to coordinate various traffic control measures such as variable speed limits, ramp metering, etc. Using a simple benchmark example from the literature we illustrate that by taking the heterogeneous nature of multi-class traffic flows into account a better performance can be obtained.
10/2009;
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ABSTRACT: We present a routing guidance approach that can be used in Intelligent Vehicle Highway Systems (IVHS). We consider IVHS consisting of automated highway systems on which intelligent vehicles organized in platoons drive to their destination, controlled by a hierarchical control framework. In this framework there are roadside controllers that provide speed and lane allocation instructions to the platoons. These roadside controllers typically manage single stretches of highways. A collection of highways is then supervised by so-called area controllers that mainly take care of the route guidance instructions for the platoons and that also coordinate the various roadside controllers in their area. In this paper we focus on the optimal route choice control problem for the area controllers. In general, this problem is a nonlinear integer optimization problem with high computational requirements, which makes the problem intractable in practice. Therefore, we first propose a simplified but fast simulation model to describe the flows of platoons in the network. Next, we show that the optimal route choice control problem can be approximated by a linear or a mixed integer linear problem. With a simple case study we illustrate that this results in a balanced trade-off between optimality and computational efficiency.
10/2009;
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ABSTRACT: We develop a day-to-day route choice control method that is based on model predictive control (MPC). For the route choice we assume that drivers base their decision on the experienced travel times. These travel times can be influenced via existing control measures, e.g. outflow limits or variable speed limits. This allows us to indirectly influence the route choice of the drivers. In previous work we have developed a route choice control method for networks with simple, non-overlapping routes, single destinations, and constant or piecewise constant flows. In this paper we extend this method to networks with overlapping routes and with restricted link inflow capacities. We illustrate the control approach with a case study for a simple network with four routes.
10/2009;
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ABSTRACT: State-of-the-art baggage handling systems trans-port luggage in an automated way using destination coded vehicles (DCVs). These vehicles transport the bags at high speeds on a "mini" railway network. Currently, the networks are simple and the performance of the system is limited. In the research we conduct, more complex networks are considered. In order to optimize the performance of the system we compare several predictive control methods that can be used to route the DCVs through the track network. More specifically, we consider centralized, decentralized, and distributed model predictive control (MPC). To assess the performance of the proposed control approaches, we consider a simple benchmark case study, in which the methods are compared for several scenarios. The results indicate that the best performance of the system is obtained when using centralized MPC. However, centralized MPC becomes intractable when the number of junctions is large due to the high computational effort this method requires. Decentralized and distributed MPC offer a balanced trade-off between computation time and optimality.
09/2009;
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ABSTRACT: Day-to-day route choice control in traffic networks with time-varying demand profiles," Abstract— We develop a day-to-day route choice control method that is based on model predictive control (MPC). To influence the route choice of drivers we propose to use traffic control measures like variable speed limits or outflow control. In previous papers we have developed MPC for route choice control in the case of a constant demand. In this paper we consider the case of a time-varying demand. The resulting MPC optimization problem is in general nonlinear and nonconvex. However, in the case of outflow control and for a linear or a piecewise affine cost function it is possible to approximate the problem and to recast it as a mixed integer linear programming (MILP) problem, for which efficient branch-and-bound solvers are available. The solution of the MILP problem can then be used as a good initial starting point for a nonlinear optimiza-tion method for the original MPC optimization problem. We also illustrate the proposed approach for a simple simulation example involving outflow control.
09/2009;
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ABSTRACT: Max-plus-linear (MPL) systems are a class of nonlinear systems that can be described by models that are ‘linear’ in the max-plus algebra. We provide here solutions to the three types of finite-horizon min–max control problems for uncertain MPL systems, depending on the nature of the control input over which we optimize: open-loop input sequences, disturbances feedback policies, and state feedback policies. We assume that the uncertainty lies in a bounded polytope and that the closed-loop input and state sequence should satisfy a given set of linear inequality constraints for all admissible disturbance realizations. Despite the fact that the controlled system is nonlinear, we provide sufficient conditions that allow one to preserve convexity of the optimal value function and its domain. As a consequence, the min–max control problems can be either recast as a linear program or solved via N parametric linear programs, where N is the prediction horizon. In some particular cases of the uncertainty description (e.g. interval matrices), by employing results from dynamic programming, we show that a min–max control problem can be recast as a deterministic optimal control problem. Copyright © 2008 John Wiley & Sons, Ltd.
International Journal of Robust and Nonlinear Control 01/2009; 19(2):218 - 242. · 1.55 Impact Factor
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CoRR. 01/2009; abs/0908.1076.
