516 research outputs found

    TRA-952: ENHANCING RESILIENCE OF TRAFFIC NETWORKS WITH CONNECTED VEHICLES

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    Improving resilience of transportation infrastructure is a multifaceted subject. One of these addresses the traffic serving capability of the transportation system. As the profession progresses in finding ways to improve infrastructure resilience in physical terms, an associated thought process is underway to enhance the adaptive capacity in traffic networks with intelligent systems and advanced related methods in order to cope with shocks in the traffic environment caused by nature-induced or other events. This paper reports research in-progress on measures for enhancing the resilience of road traffic networks with applications of connected vehicles. The need for resilient road traffic networks is defined in order to reduce the risk of severe loss of capability to serve demand. Resilience is the ability to resist the loss of traffic-serving capability by using traffic (geometric) and control system design advances (i.e. the inherent resilience) and by dynamically activating capacity-enhancing measures (i.e. the dynamic resilience). There is a need to go beyond the adaptive traffic control of intersections by enhancing inherent plus dynamic resilience of the traffic system at a broader spatial scale of a corridor or a wide-area road network. Connected vehicle technology and associated methods that yield resiliency measures (i.e. adaptive capacity attributes) are described. Ideas are advanced on how to apply these resiliency measures in practice in order to address efficiency and other issues in urban transportation. Finally, concluding remarks are presented on the technical feasibility of implementing the research ideas presented in this paper

    Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

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    Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

    On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

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    Ph.DDOCTOR OF PHILOSOPH

    Formal methods for resilient control

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    Many systems operate in uncertain, possibly adversarial environments, and their successful operation is contingent upon satisfying specific requirements, optimal performance, and ability to recover from unexpected situations. Examples are prevalent in many engineering disciplines such as transportation, robotics, energy, and biological systems. This thesis studies designing correct, resilient, and optimal controllers for discrete-time complex systems from elaborate, possibly vague, specifications. The first part of the contributions of this thesis is a framework for optimal control of non-deterministic hybrid systems from specifications described by signal temporal logic (STL), which can express a broad spectrum of interesting properties. The method is optimization-based and has several advantages over the existing techniques. When satisfying the specification is impossible, the degree of violation - characterized by STL quantitative semantics - is minimized. The computational limitations are discussed. The focus of second part is on specific types of systems and specifications for which controllers are synthesized efficiently. A class of monotone systems is introduced for which formal synthesis is scalable and almost complete. It is shown that hybrid macroscopic traffic models fall into this class. Novel techniques in modular verification and synthesis are employed for distributed optimal control, and their usefulness is shown for large-scale traffic management. Apart from monotone systems, a method is introduced for robust constrained control of networked linear systems with communication constraints. Case studies on longitudinal control of vehicular platoons are presented. The third part is about learning-based control with formal guarantees. Two approaches are studied. First, a formal perspective on adaptive control is provided in which the model is represented by a parametric transition system, and the specification is captured by an automaton. A correct-by-construction framework is developed such that the controller infers the actual parameters and plans accordingly for all possible future transitions and inferences. The second approach is based on hybrid model identification using input-output data. By assuming some limited knowledge of the range of system behaviors, theoretical performance guarantees are provided on implementing the controller designed for the identified model on the original unknown system

    Event-Triggered Sliding Mode control algorithms for a class of uncertain nonlinear systems: Experimental assessment

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    An experimental assessment of the recently introduced event-triggered sliding mode control approach is presented in this paper. The major design requirement, in this approach, is to reduce the number of transmissions over the network, while guaranteeing that the sliding mode control is stabilizing with appropriate robustness in front of matched uncertainties. In the present paper a novel Event-Triggered Sliding Mode Control algorithm is first introduced and discussed and then it is compared with two different Model-Based Event-Triggered Sliding Mode Control algorithms. Finally, their experimental assessment is reported, obtaining satisfactory performance consistent with the theoretical treatment and fulfilling all the design requirements

    Contributions to distributed MPC: coalitional and learning approaches

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    A growing number of works and applications are consolidating the research area of distributed control with partial and varying communication topologies. In this context, many of the works included in this thesis focus on the so-called coalitional MPC. This approach is characterized by the dynamic formation of groups of cooperative MPC agents (referred to as coalitions) and seeks to provide a performance close to the centralized one with lighter computations and communication demands. The thesis includes a literature review of existing distributed control methods that boost scalability and flexibility by exploiting the degree of interaction between local controllers. Likewise, we present a hierarchical coalitional MPC for traffic freeways and new methods to address the agents' clustering problem, which, given its combinatoria! nature, becomes a key issue for the real-time implementation of this type of controller. Additionally, new theoretical results to provide this clustering strategy with robust and stability guarantees to track changing targets are included. Further works of this thesis focus on the application of learning techniques in distributed and decentralized MPC schemes, thus paving the way for a future extension to the coalitional framework. In this regard, we have focused on the use of neural networks to aid distributed negotiations, and on the development of a multi­ agent learning MPC based on a collaborative data collection
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