Parametrized Model Predictive Control Approaches for Urban Traffic Networks

Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but unfortunately it has one major drawback. The, often nonlinear, optimization that has to be performed at every control time step is computationally too complex to use MPC controllers for real-time implementations (i.e. when the online optimization is performed within the control time interval of the controlled network). This paper proposes an effective parametrized MPC approach to lower the computational complexity of the MPC controller. Two parametrized control laws are proposed that can be used in the parametrized MPC framework, one based on the prediction model of the MPC controllers, and another is constructed using Grammatical Evolution (GE). The performance and computational complexity of the parametrized MPC approach is compared to a conventional MPC controller by performing an extensive simulation-based case study. The simulation results show that for the given case study the parametrized MPC approach is real-time implementable while the performance decreases with less than 3% with respect to the conventional MPC controller.Team DeSchutte

An algorithm for estimating the generalized fundamental traffic variables from point measurements using initial conditions

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

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method.Accepted Author ManuscriptDelft Center for Systems and ControlTeam DeSchutte

Timely condition-based maintenance planning for multi-component systems

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.Accepted Author ManuscriptTeam DeSchutterLearning & Autonomous Contro

A variable speed limit controller for recurrent congestion based on the optimal solution

The main goal of this paper is the proposal and simulation of a SPEed limit controller for Recurrent Traffic jams (SPERT) that approximates the behavior of an optimal controller when congestion profiles are similar to the typical one. In order to achieve this goal, the optimal solution for the typical demand profile is computed and used as a first estimation for the logic-based controller. If the real congestion differs from the typical one, the values of the speed limits are adapted by advancing or delaying their activation and deactivation. Eleven scenarios have been considered in order to test to proposed controller under different traffic conditions. The results show that the proposed controller is able to approach the optimal behavior (with a better performance that previously proposed easy-to-implement VSL control algorithms) while eliminating on-line computational cost, and increasing robustness.Hybrid, Adaptive and Nonlinea

Fault diagnosis using spatial and temporal information with application to railway track circuits

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

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.Erratum: https://doi.org/10.1016/j.apenergy.2018.06.131Team DeSchutte

A scenario-based distributed model predictive control approach for freeway networks

In this paper a scenario-based Distributed Model Predictive Control (DMPC) approach based on a reduced scenario tree is developed for large-scale freeway networks. In the new scenario-based DMPC approach, uncertainties in a large-scale freeway network are distinguished into two categories: global uncertainties for the overall network and local uncertainties applicable to subnetworks only. We propose to use a reduced scenario tree instead of using a complete scenario tree. A complete scenario tree is defined as a scenario tree consisting of global scenarios and all the combinations of the local scenarios for all subnetworks, while a reduced scenario tree is defined as a scenario tree consisting of global scenarios and a reduced local scenario tree in which local scenarios are combined within each subnetwork, not among subnetworks. Moreover, an expected-value setting and a min–max setting are considered for handling uncertainties in scenario-based DMPC. In the expected-value setting, the expected-value of the cost function values for all considered uncertainty scenarios is optimized by scenario-based DMPC. However, in the min–max setting, the worst-case of the cost function values for all considered uncertainty scenarios is optimized by scenario-based DMPC. The results for a numerical experiment show that the new scenario-based DMPC approach is effective in improving the control performance while at the same time satisfying the queue constraints in the presence of uncertainties. Additionally, the proposed approach results in a relatively low computational burden compared to the case with the complete scenario tree.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Bart De Schutte

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis: A comparative study

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.Accepted Author ManuscriptTeam DeSchutterLearning & Autonomous Contro

Timetable Scheduling for Passenger-Centric Urban Rail Networks: Model Predictive Control based on a Novel Absorption Model

Timetable scheduling plays a key role in daily operations of urban rail transit systems, as it determines the quality of service provided to passengers. In order to develop efficient timetable scheduling methods, it is necessary to develop a proper model to integrate timetable-related and passenger-related factors in urban rail network efficiently. In this paper, a novel passenger absorption model for passenger- centric urban rail networks is established. The model explicitly integrates time-varying passenger origin-destination demands and the departure frequency of each line for real-time timetable scheduling. Then, a model predictive control (MPC) method for the timetable scheduling problem is proposed based on the developed model. The resulting MPC optimization problem can be formulated as a mixed-integer programming (MILP) problem, which can be solved efficiently by using the existing MILP solvers. The effectiveness of the absorption model and the corresponding MILP-based MPC approach is illustrated through the case study based on two Beijing subway lines. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Bart De SchutterTeam Azita DabiriDelft Center for Systems and Contro