Model predictive control for freeway traffic networks

Falta palabras claveTraffic congestion on freeways is a critical problem due to higher delays, waste of fuel, a higher accident risk probability, negative impact on the environment, etc. Variable speed limits, ramp metering, and reversible lanes are some of the most often used examples of freeway traffic measures that can be used to dynamically control traffic. Nowadays, most of the dynamic traffic control systems operate according to a linear and local control loop. As explained in the thesis, the use of appropriate non-local and multivariable techniques can considerably improve the reduction in the total time spent by the drivers and other traffic performance indexes. Nonlinear centralized Model Predictive Control (MPC) is probably the best control algorithm choice for a small network as can be seen on previous references. The main practical problem of nonlinear centralized MPC is that the computational time quickly increases with the size of the network making diffcult to apply centralized MPC for large networks. Therefore, completely centralized control of large networks is viewed by most practitioners as impractical and unrealistic. The main objective of this thesis is the proposal of MPC techniques which can be applied, in practice, to real large traffic networks. Possible solutions are the use of distributed MPC (considering the network as a set of subsystems controlling each subsystem by one independent MPC), hybrid MPC (splitting the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits) or genetic algorithms (finding the fittest individuals within a generation, applying genetic operators for the recombination of those individuals, and generating a good offspring). This thesis proposes and analyses these solutions. Other open problem in freeway traffic control is the dynamic operation of reversible lanes. Despite the long history and widespread use of reversible lanes worldwide, there have been few quantitative evaluations and research studies conducted on their performance. To address this problem, this thesis proposes a macroscopic model for reversible lanes and on-line controllers for the operation of reversible lanes. Moreover, a MPC controller for freeway traffic requires a model to make accurate and reliable predictions of the traffic flow. On the other hand, this model is required to be fast enough, so that it can be used for on-line based control applications. Therefore, it is imperative to select or develop appropriate models, i.e., models that are fast and that provide accurate predictions. In this thesis, the METANET model and its extensions have been selected to be used for the prediction of the traffic flow and, based on this model, new advances in freeway traffic modeling for optimal control strategies are proposed.El ahorro de combustible, la mejora de la movilidad de los ciudadanos, la reducción de las emisiones atmosféricas y de los accidentes de tráfico son algunos de los aspectos claves en las políticas gubernamentales en el primer mundo. Durante los últimos años, un gran esfuerzo investigador se ha centrado en resolver, o mitigar, estos problemas. Debido a que la construcción de nuevos ramales viarios (o la ampliación de las ya presentes) no es siempre una opción viable (por razones económicas o técnicas), es necesaria la búsqueda de otras alternativas. Los sistemas de control dinámico de tráfico miden o estiman el estado de la circulación en cada instante y calculan la señal de control que cambia la respuesta del sistema mejorando su funcionamiento. Las señales de control de tráfico más útiles son los “ramps metering'' (o rampas de acceso controlado) y los “límites dinámicos de velocidad'' (VSL) porque son fáciles de implementar, relativamente baratos y suponen una mejora sustancial en el tiempo total de conducción empleado por los conductores (TTS). En la actualidad, la mayoría de los sistemas de control de tráfico operan usando un control clásico por realimentación, lineal y local. Sin embargo, el uso apropiado de técnicas multivariables y no locales mejorará substancialmente el comportamiento del sistema controlado. El uso de un controlador predictivo basado en modelo (MPC) centralizado es posiblemente la mejor elección para una red de tráfico pequeña. El problema fundamental del MPC centralizado es que el tiempo de computación crece exponencialmente con el tamaño de la red. Por tanto, este tipo de controladores son imposibles de implementar en tiempo real en redes suficientemente grandes. El principal objetivo de la tesis es diseñar un algoritmo de control que pueda ser calculado en tiempo real en una red viaria de gran escala minimizando, al mismo tiempo, el tiempo total de conducción empleado. Las principales contribuciones al estado del arte pueden enumerarse en: • La extensión del modelo de tráfico en autovías METANET para permitir el modelado de carriles reversibles. • Un algoritmo de identificación para los parámetros de METANET, especialmente pensado para casos donde solo hay disponible un número limitado de sensores. • El uso de una nueva definición matemática del diagrama fundamental de tráfico. • La primera comparación directa entre los dos modelos macroscópicos de tráfico más comúnmente usados. • El análisis de la robustez de los controladores predictivos aplicados a sistemas de tráfico (con respecto a variaciones de la demanda de tráfico). • La justificación de la necesidad de usar algoritmos de control globales o distribuidos (y no algoritmos locales) en sistemas de control de tráfico. • El uso de dos algoritmos predictivos distribuidos para el control de tráfico en autovías. • El diseño de un método para obtener los valores óptimos de los límites de velocidad considerando la característica discreta de los mismos y otras restricciones prácticas. • El diseño de un controlador MPC discreto para la operación de carriles reversibles. • Un algoritmo lógico fácilmente implementable para la operación de carriles reversibles.Premio Extraordinario de Doctorado U

Hybrid model predictive control for freeway traffic using discrete speed limit signals

HYCON2 Show day - Traffic modeling, Estimation and Control 13/05/2014 GrenobleIn this paper, two hybrid Model Predictive Control (MPC) approaches for freeway traffic control are proposed considering variable speed limits (VSL) as discrete variables as in current real world implementations. These discrete characteristics of the speed limits values and some necessary constraints for the actual operation of VSL are usually underestimated in the literature, so we propose a way to include them using a macroscopic traffic model within an MPC framework. For obtaining discrete signals, the MPC controller has to solve a highly non-linear optimization problem, including mixed-integer variables. Since solving such a problem is complex and difficult to execute in real-time, we propose some methods to obtain reasonable control actions in a limited computation time. The first two methods (-exhaustive and -genetic discretization) consist of first relaxing the discrete constraints for the VSL inputs; and then, based on this continuous solution and using a genetic or an exhaustive algorithm, to find discrete solutions within a distance of the continuous solution that provide a good performance. The second class of methods split the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits. The speed limits optimization, which is much more time-consuming than the ramp metering one, is solved by a genetic or an exhaustive algorithm in communication with a non-linear solver for the ramp metering. The proposed methods are tested by simulation, showing not only a good performance, but also keeping the computation time reduced.Unión Europea FP7/2007–201

Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.Unión Europea 789051Ministerio de Ciencia, Innovación y Universidades IJC2018-035395-

Logic-based traffic flow control for ramp metering and variable speed limits (Part 2: Simulation and comparison)

This paper simulates, analyzes, and compares, for two case studies (one synthetic freeway and one real-life freeway), the behavior of Logic-Based Traffic Flow Control (LB-TFC), an integrated control strategy for Ramp Metering (RM) installations and Variable Speed Limits (VSLs) that was proposed and derivated in the first part of the work (‘Part 1: Controller’). For the first case study, which was presented in the first part of the work, the control performance of LB-TFC is compared with the ones obtained with the optimal solution and with the Mainstream Traffic Flow Control (MTFC) + PI-ALINEA algorithm. Moreover, the robustness of the considered controllers is analyzed for this case study concluding that LB-TFC is quite robust, specially when comparing with MTFC + PI-ALINEA. For the second study (a stretch of the ring-road freeway SE-30 in Seville, Spain), data from 10 different days have been used in order to simulate the performance of the considered controllers using real data for the afternoon peak period. In order to properly deal with a bottleneck with a dynamically changing number of lanes, the equations used for MTFC + PI-ALINEA have been slightly modified for the second case study. For both case studies, LB-TFC provides a robust performance that, in most cases, is close to the optimal one and that improves the reduction in the Total Time Spent (TTS) obtained with MTFC + PI-ALINEA. Moreover, this paper studies the tuning of the control parameters and the advantages and disadvantages of LB-TFC

Logic-Based Traffic Flow Control for Ramp Metering and Variable Speed Limits—Part 1: Controller

This paper proposes a Logic-Based Traffic Flow Control algorithm (LB-TFC) for integrated control of Ramp Metering (RM) installations and Variable Speed Limits (VSLs) in order to reduce traffic jams created at bottlenecks. LB-TFC estimates, for each control time step, the number of vehicles that should be held back or released by the control measures (i.e. the VSLs and the RM rates) in order to avoid the capacity drop (maximizing the outflow of the bottleneck). Afterwards, based on the resulting estimated number of vehicles, the VSLs and/or the RM rates are increased or decreased in a pre-specified order. In order to avoid or reduce traffic breakdowns, the proposed controller (LB-TFC) anticipates the future evolution of the bottleneck density by using a feed-forward structure. As a result, the performance of the controller is very efficient and similar to the one obtained with an optimal controller while the implementation of the controller (with an almost instantaneous computation time) and the tuning of the parameters are easy. In the second part of this work, published in a separate paper (‘Part 2: Simulation and Comparison’), LB-TFC is simulated, analyzed and compared for two freeways (one synthetic network and one stretch of the ring-road freeway SE-30 in Seville, Spain

Centralized and distributed Model Predictive Control for the maximization of the thermal power of solar parabolic-trough plants

This paper proposes a new centralized Model Predictive Control (MPC) algorithm for the maximization of the thermal power obtained with a parabolic-trough collector field. The optimal operation of the plant is achieved by controlling a set of valves located at the beginning of each loop of collectors, which allow to outperform the response achieved with traditional control approaches for parabolic-trough plants. Unfortunately, the computational complexity of the proposed MPC controller hinders its application in real-time for medium and large parabolic-trough power plants. Consequently, this paper also proposes a logic-based distributed Model Predictive Control algorithm, which approaches the performance of the centralized MPC but entailing a much lower computational load. The proposed controllers are tested by simulation using a model of the collector field ACUREX (Almería, Spain) along a 2-h synthetic DNI profile. The results obtained show that the proposed distributed algorithm is able to perform quite close to the centralized one. Moreover, the analysis of the numerical results (in terms of achieved power) shows that the use of valves at the beginning of each loop substantially improve the achieved thermal power, that the achieved performance using a local controller is significantly lower than using a global one, and that the maximization of the thermal power does not imply the maximization or minimization of the outlet temperature

Model predictive control based on deep learning for solar parabolic-trough plants

In solar parabolic-trough plants, the use of Model Predictive Control (MPC) increases the output thermal power. However, MPC has the disadvantage of a high computational demand that hinders its application to some processes. This work proposes using artificial neural networks to approximate the optimal flow rate given by an MPC controller to decrease the computational load drastically to a 3% of the MPC computation time. The neural networks have been trained using a 30-day synthetic dataset of a collector field controlled by MPC. The use of a different number of measurements as inputs to the network has been analyzed. The results show that the neural network controllers provide practically the same mean power as the MPC controller with differences under 0.02 kW for most neural networks, less abrupt changes at the output and slight violations of the constraints. Moreover, the proposed neural networks perform well, even using a low number of sensors and predictions, decreasing the number of neural network inputs to 10% of the original size.European Research Council. Advanced Grant OCONTSOLAR number789051Ministerio de Ciencia, Innovación y Universidades IJC2018-035395-

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

This article shows how coalitional model predictive control (MPC) can be used to maximize thermal power of large-scale solar parabolic-trough plants. This strategy dynamically generates clusters of loops of collectors according to a given criterion, thus dividing the plant into loosely coupled subsystems that are locally controlled by their corresponding loop valves to gain performance and speed up the computation of control inputs. The proposed strategy is assessed with decentralized and centralized MPC in two simulated solar parabolic-trough fields. Finally, results regarding scalability are also given using these case studies.Unión Europea 789051Ministerio de Economía DPI2017-86918-RMinisterio de Ciencia, Innovación y Universidades IJC2018-035395-IMinisterio de Ciencia, Innovación y Universidades FPU18/0447

A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants

This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a -loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline.Unión Europea 78905Ministerio de Ciencia e Innovación PID2020-119476RB-I00Ministerio de Ciencia e Innovación IJC2018-035395-IMinisterio de Ciencia e Innovación FPU18/04476Ministerio de Ciencia e Innovación FPU20/0195