| 1. |
- Johansson, Alexander
(författare)
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Coordination of cross-carrier truck platooning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The need for sustainable transportation solutions is urgent as the demand for mobility of goods and people is expected to multiply in the upcoming decades. One promising solution is truck platooning, which shows great potential in reducing the energy consumption and operational costs of trucks. To utilize the benefits of truck platooning to the fullest, trucks with different schedules and routes in a road network need coordination to form platoons. This thesis addresses platoon coordination when trucks can wait at hubs to form platoons. We assume there is a reward for driving in a platoon and a cost for waiting at a hub, and the objective is to maximize the overall profit. We focus on coordinating trucks from different carriers, which is important considering that many platoon opportunities are lost if only trucks from the same carrier form platoons.In the first contributions of the thesis, we propose coordination solutions where carriers aim to maximize their own profits through cross-carrier platoon cooperation. We propose an architecture of a platoon-hailing service that stores reported platooning plans of carriers and, based on these, informs carriers about the platoons their trucks can join when they make platooning decisions. A realistic simulation study shows that the cross-carrier platooning system can achieve energy savings of 3.0% and 5.4% when 20% and 100% of the trucks are coordinated, respectively. A non-cooperative game is then formulated to model the strategic interaction among trucks with individual objectives when they coordinate for platooning and make decisions at the beginning of their journeys. The existence of at least one Nash equilibrium is shown. In the case of stochastic travel times, feedback-based solutions are developed wherein trucks repeatedly update their equilibrium decisions. A simulation study with stochastic travel times shows that the feedback-based solutions achieve platooning rates only $5\%$ lower than a solution where the travel times are known. We also explore Pareto-improving coordination guaranteeing each carrier is better off coopering with others, and models for distributing the profit within platoons.In the last contributions of the thesis, we study the problem of optimally releasing trucks at hubs when arriving according to a stochastic process, and a priori information about truck arrivals is inaccessible; this may be sensitive information to share with others. First, we study the release problem at hubs in a hub-corridor where the objective is to maximize the profit over time. The optimality of threshold-based release policies is shown under the assumption that arrivals are independent or that arrivals are dependent due to the releasing behavior at the preceding hub in the corridor. Then, we study the release problem at a single hub where the aim is to maximize the profit of trucks currently at the hub. This is realistic if trucks are only willing to wait at the hub if they can increase their own profits. Stopping time theory is used to show the optimality of a threshold-based release policy when arrivals are independent and identically distributed. These contributions show that simple coordination approaches can achieve high profits from platooning, even under limited information.
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| 2. |
- Li, Yuchao
(författare)
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Approximate Methods of Optimal Control via Dynamic Programming Models
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Optimal control theory has a long history and broad applications. Motivated by the goal of obtaining insights through unification and taking advantage of the abundant capability to generate data and perform online simulation, this thesis studies the discrete-time infinite horizon optimal control problems and introduces some approximate solution methods via abstract dynamic programming (DP) models. The proposed methods involve approximation in value space through the use of data and simulator, apply to a broad class of problems, and strike a good balance between satisfactory performance and computational expenditure.First, we consider deterministic problems with nonnegative stage costs. We derive sufficient conditions under which a local controllability condition holds for the constrained nonlinear systems, and apply the results to establish the convergence of the classical algorithms, including value iteration, policy iteration (PI), and optimistic PI. These results provide a starting point for the design of suboptimal schemes. Then we propose algorithms that take advantage of system trajectory or the presence of parallel computing units to approximate the optimal costs. These algorithms can be viewed as variants of model predictive control (MPC) or rollout, and can be applied to deterministic problems with arbitrary state and control spaces, and arbitrary dynamics. It admits extensions to problems with trajectory constraints, and a multiagent structure. Via the viewpoint provided by the abstract DP models, we also derive the performance bounds of MPC applied to unconstrained and constrained linear quadratic problems, as well as their nonlinear counterparts. These insights suggest new designs of MPC, which likely lead to larger feasible regions of the scheme while costing hardly any loss of performance measured by the costs accumulated over infinite stages. Moreover, we derive algorithms to address problems with a fixed discount factor on future costs. We apply abstract DP models to analyze $\lambda$-PI with randomization algorithms for problems with infinite policies. We show that a contraction property induced by the discount factor is sufficient for the well-posedness of the algorithm. Moreover, we identify the conditions under which the algorithm is convergent with probability one. Guided by the analysis, we exemplify a data-driven approximate implementation of the algorithm for the approximation of the optimal costs of constrained linear and nonlinear control problems. The obtained optimal cost approximations are applied in a related suboptimal scheme. Then we consider discounted problems with discrete state and control spaces and a multiagent structure. When applying rollout to address the problem, the main challenge is to perform minimization over a large control space. To this end, we propose a rollout variant that involves reshuffling the order of the agents. The approximation of the costs of base policies is through the use of on-line simulation. The proposed approach is applied to address multiagent path planning problems within a warehouse context, where through on-line replanning, the robots can adapt to a changing environment while avoiding collision with each other.
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| 3. |
- Held, Manne, 1987-
(författare)
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Optimal Speed and Powertrain Control of a Heavy-Duty Vehicle in Urban Driving
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A major challenge in the transportation industry is how to reduce the emissions of greenhouse gases. One way of achieving this in vehicles is to drive more fuel-efficiently. One recently developed technique that has been successful in reducing the fuel consumption is the look-ahead cruise controller, which utilizes future conditions such as road topography. In this this thesis, similar methods are used in order to reduce the fuel consumption of heavy-duty vehicles driving in environments where the required and desired velocity vary. The main focus is on vehicles in urban driving, which must alter their velocity due to, for instance, changing legal speed restrictions and the presence of intersections. The driving missions of such vehicles are here formulated as optimal control problems. In order to restrict the vehicle to drive in a way that does not deviate too much from a normal way of driving, constraints on the velocity are imposed based on statistics from real truck operation.In a first approach, the vehicle model is based on forces and the cost function involves the consumed energy. This problem is solved both offline using Pontryagin's maximum principle and online using a model predictive controller with a quadratic program formulation. Simulations show that 7 % energy can be saved without increasing the trip time nor deviating from a normal way of driving.In a second approach, the vehicle model is extended to include an engine and a gearbox with the objective of minimizing the fuel consumption. A fuel map for the engine and a polynomial function for the gearbox losses are extracted from experimental data and used in the model. This problem is solved using dynamic programming taking into consideration gear changes, coasting with gear and coasting in neutral. Simulations show that by allowing the use of coasting in neutral gear, 13 % fuel can be saved without increasing the trip time or deviating from a normal way of driving.Finally, an implementation of a rule-based controller into an advanced vehicle model in highway driving is performed. The controller identifies sections of downhills where fuel can be saved by coasting in neutral gear.
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| 4. |
- Pereira, Goncalo Collares
(författare)
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Adaptive Lateral Model Predictive Control for Autonomous Driving of Heavy-Duty Vehicles
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous Vehicle (AV) technology promises safer, greener, and more efficient means of transportation for everyone. AVs are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where Heavy-Duty Vehicles (HDVs) operate. This thesis addresses lateral motion control for autonomous HDVs using Model Predictive Control (MPC). Lateral control for HDVs still has many open questions to be addressed, in particular, precise path tracking while ensuring a smooth, comfortable, and stable ride, coping with both external and internal disturbances, and adapting to different vehicles and conditions.To address these challenges, a comprehensive control module architecture is designed to adapt seamlessly to different vehicle types and interface with various planning and localization modules. Furthermore, it is designed to address system delays, maintain certain error bounds, and respect actuation constraints.This thesis presents the Reference Aware MPC (RA-MPC) for autonomous vehicles. This controller is iteratively improved throughout the thesis. The RA-MPC introduces a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller uses the linear time-varying MPC framework and considers control input rate and acceleration constraints to account for steering limitations. Furthermore, multiple models and control inputs are considered throughout the thesis. Ultimately, curvature acceleration is used as the control input, which together with stability ingredients, allows for stability guarantees under certain conditions via Lyapunov techniques.MPC is highly dependent on the prediction model used. This thesis proposes and compares different models. First, an offline-fitted, vehicle-specific nonlinear curvature response function is proposed and integrated into the kinematic bicycle model. The curvature response function is modeled as two Gaussian functions. To enhance the model's versatility and applicability to a fleet of vehicles the nonlinear curvature response table kinematic model is presented. This model replaces the function with a table, which is estimated online by means of Kalman filtering, adapting to the current vehicle and operating conditions.All controllers and models are simulated and experimentally validated on Scania HDVs and iteratively compared to the previous state-of-the-art. The RA-MPC with the nonlinear curvature response table kinematic model is shown to be the best for the problems and conditions considered. The robustness and adaptiveness of the proposed approach are highlighted by testing different vehicle configurations (a haulage truck, a mining truck, and a bus), operating conditions, and scenarios. The model allows all vehicles to accomplish the scenarios with very similar performance. Overall, the results show an average absolute lateral error to path no bigger than 7 cm, and a worst-case deviation no bigger than 25 cm. These results demonstrate the controller's ability to handle a fleet of HDVs, without the need for vehicle-specific tuning or intervention from expert engineers.
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| 5. |
- Rylander, Lina, 1990-
(författare)
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Designing for Change in Complex Systems : Design Considerations for Uptime in a Transportation System with Driverless Vehicles
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The transportation system is undergoing a transformation to enable socially, environmentally, and economically sustainable transport solutions, and driverless trucks are considered one innovation that can contribute to a more sustainable and efficient transportation system. Nevertheless, introducing driverless trucks will cause disruptions in the system, and one considerable change is that the driver is no longer present. The effects of removing the driver from the transportation system are little explored, but it is reasonable to argue that it will affect the system design, such as how system actors interact, their relationships, and how they need to be organized.The fault-handling system is one crucial subsystem that enables uptime in the transportation system and is the system that provides activities that maintain vehicle health. Such activities can be maintenance, repair, and vehicle monitoring services. However, the fault-handling system provides service centers with experienced technicians, diagnosis and troubleshooting tools, maintenance planning support, and fleet management. Thus, maintenance and repair can be put in the context of a service.Service design methods have been applied in this thesis to generate insights regarding the fault-handling system today and to develop a concept of how a future system for driverless trucks could be designed. The study has involved interviews with system actors, generating patterns, and understanding the system today, and Scania experts have been engaged in creating scenarios. Later, those were used during a workshop to explore the present system and co-create a desired future. Moreover, a prototype was developed to perform interventions. This thesis has two purposes: to explore how design methods can contribute to changing complex socio-technical systems, such as the transportation system, and to explore what design considerations are needed to support uptime when manually driven trucks become driverless. The questions explored are: how can design methods be used to contribute to changes in socio-technical systems, such as the transportation system? How may a system for fault-handling and decision-making be designed to support uptime in a transportation system with driverless vehicles? What is the driver's role concerning uptime in the transportation system?The driver's role in the fault-handling system can give insight into how the system is structured today, such as existing mental models, relationships, institutional arrangements, and other aspects to consider when redesigning it for driverless trucks. The study showed that the driver has a significant role in the fault-handling system considering five themes: 1) fault detection, 2) decision-making, 3) information exchange, 4) information retrieval, and 5) tacit knowledge and experience. The themes were further developed into considerations for redesigning the fault-handling system for driverless trucks.This thesis contributes to Scania's development as a provider of sustainable transport solutions and, specifically, how the fault-handling system can be designed for the future transport system. It also gives insights into how design can be used as a tool at Scania when studying disruptive innovation. The results have derived new insights and questioned existing assumptions regarding the fault-handling system, which can be the beginning of questioning existing mental models in the organization because of the change. Keywords: Driverless vehicles, vehicle health management, service design, system transformation, complex socio-technical system.
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| 6. |
- Chen, Xiao
(författare)
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Safe Intersection and Merging Coordination of Connected and Automated Vehicles
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Connected and automated vehicles (CAVs) are a transformative technology that promises to bring innovative solutions to transportation systems. One of their significant advantages is the elimination of human factors, which makes them capable of resolving the congestion problem prevalent in areas such as ramp merging points and road intersections. Through the sharing of information between vehicles and with the infrastructure, CAVs can coordinate their cross-time cooperatively. This collaborative approach results in avoiding unnecessary stops, which leads to more efficient utilization of the road infrastructure and improved safety for all road participants.In this licentiate thesis work, we study the problem of formulating coordination strategies for CAVs to efficiently traverse through conflicting regions while maintaining safety with other road participants such as other CAVs or platoons of CAVs or human-driven vehicles (HDVs). We emphasize the challenges when platoons or HDVs are involved and develop coordination solutions on various scales accordingly. We start by considering a highway ramp merging scenario that involves platoons of CAVs. In such scenarios, vehicles in a platoon drive in close proximity and create moving barriers for merging traffic. To address this challenge, we propose a bi-level coordination framework. A central coordinator schedules the merging time and speed for all CAVs by solving a mixed integer linear programming (MILP) problem, optimizing traffic performance while maintaining platoon formation. This assigned schedule is executed by each individual vehicle at the control level. When integrated, this framework allows platoons to split occasionally for the merging vehicles, balancing traffic throughput with platoon formation.In intersections where mixed traffic is present, CAVs need to anticipate the driving behavior of HDVs accurately to plan a safe future trajectory. Due to the unpredictable nature of human behavior, we propose an invariant safe model predictive control (MPC) that considers worst-case scenarios using forward reachable sets to guarantee safety at all times. In addition, to compensate for conservatism, we apply a contingency MPC (CMPC) framework with parallel horizons. One horizon is for safety guarantees in contingency, while the other is for optimizing performance. The methods are applied to general intersection problems through distributed implementation and produce safe and efficient coordination results.Lastly, we examine the challenges that arise in the real-life implementation of the proposed coordination strategies. To account for potential occlusions, estimation errors, and communication defects, we propose a communication-based and integrated framework from estimation to control. Through set estimation and reachability analysis, we generate robust set estimations of surrounding vehicles and integrate this information with the coordination stack to ensure safe navigation through intersections in an experimental setting.
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| 7. |
- Mårtensson, Jonas, 1976-
(författare)
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Geometric analysis of stochastic model errors in system identification
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Models of dynamical systems are important in many disciplines of science, ranging from physics and traditional mechanical and electrical engineering to life sciences, computer science and economics. Engineers, for example, use models for development, analysis and control of complex technical systems. Dynamical models can be derived from physical insights, for example some known laws of nature, (which are models themselves), or, as considered here, by fitting unknown model parameters to measurements from an experiment. The latter approach is what we call system identification. A model is always (at best) an approximation of the true system, and for a model to be useful, we need some characterization of how large the model error is. In this thesis we consider model errors originating from stochastic (random) disturbances that the system was subject to during the experiment. Stochastic model errors, known as variance-errors, are usually analyzed under the assumption of an infinite number of data. In this context the variance-error can be expressed as a (complicated) function of the spectra (and cross-spectra) of the disturbances and the excitation signals, a description of the true system, and the model structure (i.e., the parametrization of the model). The primary contribution of this thesis is an alternative geometric interpretation of this expression. This geometric approach consists in viewing the asymptotic variance as an orthogonal projection on a vector space that to a large extent is defined from the model structure. This approach is useful in several ways. Primarily, it facilitates structural analysis of how, for example, model structure and model order, and possible feedback mechanisms, affect the variance-error. Moreover, simple upper bounds on the variance-error can be obtained, which are independent of the employed model structure. The accuracy of estimated poles and zeros of linear time-invariant systems can also be analyzed using results closely related to the approach described above. One fundamental conclusion is that the accuracy of estimates of unstable poles and zeros is little affected by the model order, while the accuracy deteriorates fast with the model order for stable poles and zeros. The geometric approach has also shown potential in input design, which treats how the excitation signal (input signal) should be chosen to yield informative experiments. For example, we show cases when the input signal can be chosen so that the variance-error does not depend on the model order or the model structure. Perhaps the most important contribution of this thesis, and of the geometric approach, is the analysis method as such. Hopefully the methodology presented in this work will be useful in future research on the accuracy of identified models; in particular non-linear models and models with multiple inputs and outputs, for which there are relatively few results at present.
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| 8. |
- Pereira, Goncalo Collares
(författare)
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Lateral Model Predictive Control for Autonomous Heavy-Duty Vehicles : Sensor, Actuator, and Reference Uncertainties
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous vehicle technology is shaping the future of road transportation. This technology promises safer, greener, and more efficient means of transportation for everyone. Autonomous vehicles are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where heavy-duty vehicles (HDVs) operate. Although research for autonomous systems has boomed in recent years, there are still many challenges associated with them. This thesis addresses lateral motion control for autonomous HDVs using model predictive control (MPC).First, the autonomous vehicle architecture and, in particular, the control module architecture are introduced. The control module receives the current vehicle states and a trajectory to follow, and requests a velocity and a steering-wheel angle to the vehicle actuators. Moreover, the control module needs to handle system delays, maintain certain error bounds, respect actuation constraints, and provide a safe and comfortable ride.Second, a linear robust model predictive controller for disturbed discrete-time nonlinear systems is presented. The optimization problem includes the initial nominal state of the system, which allows to guarantee robust exponential stability of the disturbance invariant set for the discrete-time nonlinear system. The controller effectiveness is demonstrated through simulations of an autonomous vehicle lateral control application. Finally, the controller limitations and possible improvements are discussed with the help of a more constrained autonomous vehicle example.Third, a path following reference aware MPC (RA-MPC) for autonomous vehicles is presented. The controller makes use of the linear time-varying MPC framework, and considers control input rates and accelerations to account for limitations on the vehicle steering dynamics and to provide a safe and comfortable ride. Moreover, the controller includes a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller is verified through simulations and through experiments with a Scania construction truck. The experiments show an average lateral error to path of around 7 cm, not exceeding 27 cm on dry roads.Finally, the nonlinear curvature response of the vehicle is studied and the MPC prediction model is modified to account for it. The standard kinematic bicycle model does not describe accurately the lateral motion of the vehicle. Therefore, by extending the model with a nonlinear function that maps the curvature response of the vehicle to a given request, a better prediction of the vehicle's movement is achieved. The modified model is used together with the RA-MPC and verified through simulations and experiments with a Scania construction truck, where the improvements of the more accurate model are verified. The experiments show an average lateral error to path of around 5 cm, not exceeding 20 cm on wet roads.
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| 9. |
- Tao, Xin, 1992-
(författare)
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Application of Integrated Vehicle Health Management in Automated Decision-making for Driverless Vehicles
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Vehicles are becoming increasingly complex and are prone to faults and failures, which threaten the dependability of vehicles in terms of availability, reliability, safety, and security. When vehicles are detected with certain types of faults and get into alarm situations, human drivers play a vital role in deciding what strategies and actions to take. Once driverless vehicles are introduced, human drivers' roles in decision-making will no longer exist, which urges new solutions on both technological and managerial levels. This thesis depicts the current human decision-making process by analyzing field study data in the truck industry, which contributes to gaining domain knowledge and identifying research gaps. An integrated vehicle health management scheme is applied to automate this decision-making process by integrating vehicle health state estimation and prediction, resource utilization, and self-adaptive management. To implement this scheme, fault diagnosis and decision-making methods are proposed, and a decision support system is designed. Fault diagnosis is a critical functional module for providing reliable vehicle health state information for decision-making. To address the influence of uncertainties in fault diagnosis, we propose an uncertainty analysis framework and a fault diagnosis method using Bayesian inference.Simulation experiments validate that the proposed method could effectively diagnose the root cause of fault symptoms under environmental uncertainty. A risk-based automated decision-making method is presented, which imitates the human decision-making process.On this basis, a collaborative decision-making method is proposed by considering traffic congestion, which is a currently neglected public concern.Experiment results show that the proposed methods could effectively reduce the economic risk and the risk of traffic congestion.In the end, a decision support system is designed to provide decision information to its human users. Besides, reviewing and learning functions are considered for gaining knowledge and achieving full automation in the long run. Additional system stakeholders from the public sector regarding safety, traffic, and the environment are considered. A transparent, interactive, and adaptive graphical user interface of the system is designed to enhance user experience and trust.This thesis shows the potential of automated decision-making and technical system design in increasing corporate profits, catalyzing public-private partnerships, enabling technological transformation, and achieving a more sustainable transportation system.
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| 10. |
- Wiltz, Adrian
(författare)
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Distributed Control for Spatio-Temporally Constrained Systems
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we develop methods leading towards the distributed control of spatio-temporally constrained systems. Overall, we focus on two different approaches: a model predictive control approach and an approach based on ensuring set-invariance via control barrier functions. Developing a distributed control framework for spatio-temporally constrained systems is challenging since multiple subsystems are interconnected via time-varying state constraints. Often, such constraints are only implicitly given as logic formulas, for example in Signal Temporal Logic (STL).Our approach to dealing with spatio-temporal constraints is as follows. We aim at combining the computational efficiency of low-level feedback controllers with planning algorithms. Low-level feedback controllers shall ensure the satisfaction of parts of spatio-temporal constraints such as coupling state constraints or short term time-constraints. In contrast, planning algorithms account for those parts that require computationally intense planning operations. Powerful low-level controllers can simplify the planning task significantly. For this reason, the focus of this thesis is on the development of low level feedback controllers. In the first part, we focus on handling coupling state constraints using a model predictive control (MPC) approach. Commonly, the distributed handling of coupling state constraints requires a sequential or iterative MPC scheme which however is computationally time-intense. We address this issue by employing consistency constraints to develop a parallelized distributed model predictive controller (DMPC). By using consistency constraints, each subsystem guarantees to its neighbors that its states stay within a particular neighborhood around a reference trajectory. Furthermore, we propose extensions to robust and iterative schemes. Building up on this, also systems with bounded dynamic couplings can be controlled.In the second part, we focus on methods for ensuring set-invariance. In particular, we focus on control barrier functions (CBF). We show how spatio-temporal constraints that comprise disjunctions (logic OR) can be encoded in non-smooth time-varying control barrier functions and how subgradients can be used to synthesize an efficient gradient-based controller. For these results, controllability assumptions must be invoked. To extend the results to systems with weaker controllability properties, we investigate the connection between controllability properties and the construction of CBFs. As a result, we propose a construction method for CBFs based on finite horizon predictions. The constructed CBF exhibits favorable properties for the extension of the previous results on encoding spatio-temporal constraints in CBFs to systems with weaker controllability properties. At last, we investigate with a case study how set-invariance methods can be used to implicitly coordinate systems subject to coupled state constraints. Our proposed method is fully decentralized and subsystems coordinate themselves purely via their actions and the adjustment of their individual constraints.In the end, we draw a conclusion and outline how the presented results contribute to the development of a distributed control framework for spatio-temporally constrained systems.
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| 11. |
- Zhao, Lin, 1995-, et al.
(författare)
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Study of different steering feedback models influence during remote driving
- 2021
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Ingår i: Proceedings of the 27th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks. - Cham : Springer International Publishing.
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Konferensbidrag (refereegranskat)abstract
- Steering feedback is one essential aspect to provide real world information, and can influence driving performance during remote driving. In this work, the classical feedback models based on physical characteristics (Physical Model) and modular characteristics (Modular Model) of the steering system are constructed separately, and the influences of it on the remote drivers are studied. Objective and subjective measurement methods are separately used for evaluating the performance of the feedback models. In the subjective assessment, a multi-level assessment method is used for studying the influence of steering models on driver’s intuitive feeling. In the objective assessment, lane following accuracy, steering reversal rates, vehicle speed, time consumption, and throttle engagement are studied for different feedback models and scenarios. Moreover, the human biological information of electroencephalogram and heart rate variability are measured for studying the workload differences. The results showed that the physical model gave drivers a better steering characteristic feel and confidence in remote driving while the modular model could provide better real world feel. Returnability was an important parameter in remote driving, and the level of feedback force and returnability speed could be lower in remote driving compared to real car driving. It was also found that drivers had a higher workload in remote driving compared to real car driving.
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