Stability analysis of a stochastic port-Hamiltonian car-following model

Port-Hamiltonian systems are pertinent representations of many non-linear physical systems. In this article, we formulate and analyse a general class of stochastic car-following models having a systematic port-Hamiltonian structure. The model class is a generalisation of classical car-following approaches, including the Optimal Velocity model by Bando et al. (1995), the Full Velocity Difference model by Jiang et al. (2001), and recent stochastic following models based on the Ornstein-Uhlenbeck process. In contrast to traditional models for which the interaction is totally asymmetric (i.e., depending only on the speed and distance to the predecessor), the port-Hamiltonian car-following model also depends on the distance to the follower. We determine the exact stability condition of the finite system with $N$ vehicles and periodic boundaries. The stable system is ergodic with a unique Gaussian invariant measure. Other model properties are studied using numerical simulation. It turns out that the Hamiltonian component improves the flow stability, reducing the total energy in the system. Furthermore, it prevents the problematic formation of stop-and-go waves with periodic dynamics, even in the presence of stochastic perturbations.Comment: 23 pages, 4 figure

Stabilisation of stochastic single-file dynamics using port-Hamiltonian systems

This study revisits a recently proposed symmetric port-Hamiltonian single-file model in one dimension. The uniform streaming solutions are stable in the deterministic model. However, the introduction of white noise into the dynamics causes the model to exhibit divergence. In response, we introduce a control term in a port-Hamiltonian framework. Our results show that this control term effectively stabilises the dynamics even in the presence of noise, providing valuable insights for the control of road traffic flows

Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG)

Saving Fuel for Heavy-Duty Vehicles Using Connectivity and Automation

The booming of e-commerce is placing an increasing burden on freight transport system by demanding faster and larger amount of delivery. Despite the variety in freight transport means, the dominant freight transport method is still ground transport, or specifically, transport by heavy-duty vehicles. Roughly one-third of the annual ground freight transport expense goes to fuel expenses. If fuel costs could be reduced, the finance of freight transport would be improved and may increase the transport volume without additional charge to average consumers. A further benefit of reducing fuel consumption would be the related environmental impact. The fuel consumption of the heavy-duty vehicles, despite being the minority of road vehicles, has a major influence on the whole transportation sector, which is a major contributor to greenhouse gas emissions. Thus, saving fuel for heavy-duty trucks would also reduce greenhouse gas emission, leading to environmental benefits. For decades, researchers and engineers have been seeking to improve the fuel economy of heavy-duty vehicles by focusing on vehicles themselves, working on advancing the vehicle design in many aspects. More recently, attention has turned to improve fuel efficiency while driving in the dynamic traffic environment. Fuel savings effort may be realized due to advancements in connected and automated vehicle technologies, which provide more information for vehicle design and control. This dissertation presents state-of-the-art techniques that utilize connectivity and automation to improve the fuel economy of heavy-duty vehicles, while allowing them to stay safe in real-world traffic environments. These techniques focus on three different levels of vehicle control, and can result in significant fuel improvements at each level. Starting at the powertrain level, a gear shift schedule design approach is proposed based on hybrid system theory. The resulting design improves fuel economy without comprising driveability. This new approach also unifies the gear shift logic design of human-driven and automated vehicles, and shows a large potential in fuel saving when enhanced with higher level connectivity and automation. With this potential in mind, at the vehicle level, a fuel-efficient predictive cruise control algorithm is presented. This mechanism takes into account road elevation, wind, and aggregated traffic information acquired via connectivity. Moreover, a systematic tool to tune the optimization parameters to prioritize different objectives is developed. While the algorithm and the tool are shown to be beneficial for heavy-duty vehicles when they are in mild traffic, such benefits may not be attainable when the traffic is dense. Thus, at the traffic level, when a heavy-duty vehicle needs to interact with surrounding vehicles in dense traffic, a connected cruise control algorithm is proposed. This algorithm utilizes beyond-line-of-sight information, acquired through vehicle-to-vehicle communication, to gain a better understanding of the surrounding traffic so that the vehicle can response to traffic in a fuel efficient way. These techniques can bring substantial fuel economy improvements when applied individually. In practice, it is important to integrate these three techniques at different levels in a safe manner, so as to acquire the overall benefits. To achieve this, a safety verification method is developed for the connected cruise control, to coordinate the algorithms at the vehicle level and the traffic level, maximizing the fuel benefits while staying safe.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147523/1/hchaozhe_1.pd

Finite-time disturbance reconstruction and robust fractional-order controller design for hybrid port-Hamiltonian dynamics of biped robots

In this paper, disturbance reconstruction and robust trajectory tracking control of biped robots with hybrid dynamics in the port-Hamiltonian form is investigated. A new type of Hamiltonian function is introduced, which ensures the finite-time stability of the closed-loop system. The proposed control system consists of two loops: an inner and an outer loop. A fractional proportional-integral-derivative filter is used to achieve finite-time convergence for position tracking errors at the outer loop. A fractional-order sliding mode controller acts as a centralized controller at the inner-loop, ensuring the finite-time stability of the velocity tracking error. In this loop, the undesired effects of unknown external disturbance and parameter uncertainties are compensated using estimators. Two disturbance estimators are envisioned. The former is designed using fractional calculus. The latter is an adaptive estimator, and it is constructed using the general dynamic of biped robots. Stability analysis shows that the closed-loop system is finite-time stable in both contact-less and impact phases. Simulation studies on two types of biped robots (i.e., two-link walker and RABBIT biped robot) demonstrate the proposed controller's tracking performance and disturbance rejection capability

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning

A runtime assurance system (RTA) for a given plant enables the exercise of an untrusted or experimental controller while assuring safety with a backup (or safety) controller. The relevant computational design problem is to create a logic that assures safety by switching to the safety controller as needed, while maximizing some performance criteria, such as the utilization of the untrusted controller. Existing RTA design strategies are well-known to be overly conservative and, in principle, can lead to safety violations. In this paper, we formulate the optimal RTA design problem and present a new approach for solving it. Our approach relies on reward shaping and reinforcement learning. It can guarantee safety and leverage machine learning technologies for scalability. We have implemented this algorithm and present experimental results comparing our approach with state-of-the-art reachability and simulation-based RTA approaches in a number of scenarios using aircraft models in 3D space with complex safety requirements. Our approach can guarantee safety while increasing utilization of the experimental controller over existing approaches

Online Control of Automotive systems for improved Real-World Performance

[ES] La necesidad de mejorar el consumo de combustible y las emisiones de los sistemas propulsivos de automoción en condiciones reales de conducción es la base de esta tesis. Para ello, se exploran dos ejes: En primer lugar, el control de los sistemas de propulsión. El estado del arte de control en los sistemas propulsivos de automoción se basa en gran medida en el uso de técnicas de optimización que buscan las leyes de control que minimizan una función de coste en un conjunto de condiciones de operación denidas a priori. Estas leyes se almacenan en las ECUs de producción en forma de mapas de calibración de los diferentes actuadores del motor. Las incertidumbres asociadas al conjunto limitado de condiciones en el proceso de calibración dan lugar a un funcionamiento subóptimo del sistema de propulsión en condiciones de conducción real. Por lo tanto, en este trabajo se proponen métodos de control adaptativo que optimicen la gestión de la planta propulsiva a las condiciones esperadas de funcionamiento para un usuario y un caso determinado en lugar de a un conjunto genérico de condiciones. El segundo eje se reere a optimizar, en lugar de los parámetros de control del sistema propulsivo, la demanda de potencia de este, introduciendo al propio conductor en el bucle de control, sugiriéndole las acciones a tomar. En particular, este segundo eje se reere al control de la velocidad del vehículo (conocido popularmente como Eco-Driving en la literatura) en condiciones reales de conducción. Se proponen sistemas de aviso en tiempo real al conductor acerca de la velocidad óptima para minimizar el consumo del vehículo. Los métodos de control desarrollados para cada aplicación se describen en detalle en la tesis y se muestran ensayos experimentales de validación en los casos de estudio diseñados. Ambos ejes representan un problema de control óptimo, denido por un sistema dinámico, unas restricciones a cumplir y un coste a minimizar, en este sentido las herramientas desarrolladas en la tesis son comunes a los dos ejes: Un modelo de vehículo, una herramienta de predicción del ciclo de conducción y métodos de control óptimo (Programación Dinámica, Principio Mínimo de Pontryagin y Estrategia de Consumo Equivalente Mínimo). Dependiendo de la aplicación, los métodos desarrollados se implementaron en varios entornos experimentales: un motor térmico en sala de ensayos simulando el resto del vehículo, incluyendo el resto del sistema de propulsión híbrido y en un vehículo real. Los resultados muestran mejoras signicativas en el rendimiento del sistema de propulsión en términos de ahorro de combustible y emisiones en comparación con los métodos empleados en el estado del arte actual.[CA] La necessitat de millorar el consum de combustible i les emissions dels sistemes propulsius d'automoció en condicions reals de conducció és la base d'aquesta tesi. Per a això, s'exploren dos eixos: En primer lloc, el control dels sistemes de propulsió. L'estat de l'art de control en els sistemes propulsius d'automoció es basa en gran manera en l'ús de tècniques d'optimització que busquen les lleis de control que minimitzen una funció de cost en un conjunt de condicions d'operació denides a priori. Aquestes lleis s'emmagatzemen en les Ecus de producció en forma de mapes de calibratge dels diferents actuadors del motor. Les incerteses associades al conjunt limitat de condicions en el procés de calibratge donen lloc a un funcionament subòptim del sistema de propulsió en condicions de conducció real. Per tant, en aquest treball es proposen mètodes de control adaptatiu que optimitzen la gestió de la planta propulsiva a les condicions esperades de funcionament per a un usuari i un cas determinat en lloc d'un conjunt genèric de condicions. El segon eix es refereix a optimitzar, en lloc dels paràmetres de control del sistema propulsiu, la demanda de potència d'aquest, introduint al propi conductor en el bucle de control, suggerint-li les accions a prendre. En particular, aquest segon eix es refereix al control de la velocitat del vehicle (conegut popularment com Eco-*Driving en la literatura) en condicions reals de conducció. Es proposen sistemes d'avís en temps real al conductor sobre la velocitat òptima per a minimitzar el consum del vehicle. Els mètodes de control desenvolupats per a cada aplicació es descriuen detalladament en la tesi i es mostren assajos experimentals de validació en els casos d'estudi dissenyats. Tots dos eixos representen un problema de control òptim, denit per un sistema dinàmic, unes restriccions a complir i un cost a minimitzar, en aquest sentit les eines desenvolupades en la tesi són comunes als dos eixos: Un model de vehicle, una eina de predicció del cicle de conducció i mètodes de control òptim (Programació Dinàmica, Principi Mínim de *Pontryagin i Estratègia de Consum Equivalent Mínim). Depenent de l'aplicació, els mètodes desenvolupats es van implementar en diversos entorns experimentals: un motor tèrmic en sala d'assajos simulant la resta del vehicle, incloent la resta del sistema de propulsió híbrid i en un vehicle real. Els resultats mostren millores signicatives en el rendiment del sistema de propulsió en termes d'estalvi de combustible i emissions en comparació amb els mètodes emprats en l'estat de l'art actual.[EN] The need of improving the real-world fuel consumption and emission of automotive applications is the basis of this thesis. To this end, two verticals are explored: First is the online control of the powertrain systems. In state-of-the-art Optimal Control techniques (such as Dyanmic Programming, Pontryagins Minimum Principle, etc...) are extensively used to formulate the optimal control laws. These laws are stored in the production ECUs in the form of feedforward calibration maps. The unaccounted uncertainities related to the real-world during the powertrain calibration result in suboptimal operations of the powertrain in actual driving. Therefore, adaptive control methods are proposed in this work which, optimise the energy management of the conventional and the HEV powertrain control on real driving mission. The second vertical is regarding the vehicle speed control (popularly known as Eco-Driving in the literature) methods in real driving condition. In particular, speed advisory systems are proposed for real time application on a vehicle. The control methods developed for each application are described in details with their verication and validation on the designed case studies. Apart from the developed control methods, there are three tools that were developed and used at various stages of this thesis: A vehicle model, A driving cycle prediction tool and optimal control methods (dynamic programming, PMP and ECMS). Depending on the application, the developed methods were implemented on the Hardware-In-Loop Internal Combustion Engine testing setup or on a real vehicle. The results show signicant improvements in the performance of the powertrain in terms of fuel economy and emissions in comparison to the state-of-the-art methods.Pandey, V. (2021). Online Control of Automotive systems for improved Real-World Performance [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/173716TESI

Robust String Stability of Vehicle Platoons with Communication

This work investigates longitudinal spacing policies and vehicular communication strategies that can reduce inter-vehicular spacing between the vehicles of automated highway platoons, in the presence of parasitic actuation lags. Currently employed platooning technologies rely on the vehicle’s onboard sensors for information of the neighboring vehicles, due to this they may require large spacing between the vehicles to ensure string stability in the presence of uncertainties, such as parasitic actuation lags. More precisely, they require that the minimum employable time headway (hmin) must be lower bounded by 2τ₀ for string stability, where τ₀ is the maximum parasitic actuation lag. Recent studies have demonstrated that using vehicular communication one may be able to employ smaller spacing between vehicles while ensuring robustness to parasitic lags. However, precise results on the extent of such reduction are sparse in the literature. In this work, platoon string stability is used as a metric to study controllers that require vehicular communication, and find the amount of reduction in spacing such controllers can offer. First, the effects of multiple vehicle look ahead in vehicle platoons that employ a Constant Spacing Policy (CSP) based controller without lead vehicle information in the presence of parasitic lags is studied and string instability of such platoons is demonstrated. A robustly string stable CSP controller that employs information from the leader and the immediate predecessor is considered to determine an upper bound on the allowable parasitic lag; for this CSP controller, a design procedure for the selection of controller gains for a given parasitic lag is also provided. For a string of vehicles adopting a Constant Time Headway Policy (CTHP), it is demonstrated that the minimum employable time headway can be further decreased via vehicular communication in the following manner: (1) if the position, velocity and acceleration of the immediate predecessor vehicle is used, then the ii minimum employable time headway hmin can be reduced to τ₀; (2) if the position and velocity information of r immediately preceding vehicles is used, then hmin can be reduced to 4τ₀/(1 + r); (3) furthermore, if the acceleration of ‘r’ immediately preceding vehicles is used, then hmin can be reduced to 2τ₀/(1 + r); and (4) if the position, velocity and acceleration of the immediate and the r-th predecessors are used, then hmin = 2τ₀/(1 + r). Note that cases (3) and (4) provide the same lower bound on the minimum employable time headway; however, case (4) requires much less communicated information. Representative numerical simulations that are conducted to corroborate the above results are discussed. Vehicle formations employing ring structured communication strategies are also studied in this work and a combinatorial approach for developing ring graphs for vehicle formations is proposed. Stability properties of the platoons with ring graphs, limitations of using ring graphs in platoons, and methods to overcome such limitations are explored. In addition, with ring communication structure, it is possible to devise simple ways to recon- figure the graph when vehicles are added to or removed from the platoon or formation, which is also discussed in this work. Further, experimental results using mobile robots for platooning and two-dimensional formations using ring graphs are discussed