<p>QC 20150209</p>

Heavy-duty vehicle (HDV) manufacturers and fleet owners are facing great challenges for a maintained sustainable transport system as the demand for road freight transport is continuously increasing. HDV platooning is one potential solution topartially mitigate the environmental impacts as well as to reduce the fuel consumption, improve safety, and increase the throughput on congested highways. Although the concept of vehicle platooning has existed for decades, it has only been recently possible to implement in practice. Advancement in information and communications technology as well as in on-board technology allow the vehicles to connect with each other and the infrastructure. As goods have different origins, destinations, and time restrictions, it is not evident how the HDVs can fully utilize the platooning benefits during transport missions. There is a need to systematically coordinate scattered vehicles on the road network to form platoons in order to maximize the benefits of platooning. This thesis presents a framework for the coordination of HDV platoon formations. The focus lies on analyzing and validating the possibility to form platoons through fuel-efficient coordination decisions. A functional architecture for goods transport is presented, which divides the overall complex transport system into manageable layers. A vehicle model is developed to compute the impact a coordination decision has on the fuel cost. Platoon coordination consists of rerouting vehicles, adjusting departure times, and adjusting speed profiles. The focus in this thesis is on adjusting vehicles’ speeds through catch-up coordination. The first main contribution of the thesis is the investigation of how and when a pair of vehicles should form platoons given their position, speed, and destination. We derive a break-even ratio where the fuel cost of catching up and platooning is equal to the fuel cost of maintaining the original profile. By comparing the distance to destination and the distance to the candidate vehicle ahead with the break-even ratio, we can conclude whether a catch-up coordination would be beneficial or not. We also show that the road topography has little or no impact on the fuel savings of catch-up coordination. The second contribution is the study of extending the catch-up coordination into a road network with scattered vehicles with the possibility to form platoons and plan routes on junctions. Incoming vehicles on a road junction are aware of other incoming vehicles and of their position, speed, and destination. The vehicles can decide if a platoon should be formed and which path to take. Simulations on the German road network show fuel savings exceeding 5% with a few thousand vehicles. For our third contribution, we use real vehicle probe data obtained from a fleet management system to investigate how catch-up coordination and departure time adjustments can increase the fuel savings from today’s spontaneous platooning. The results show that coordination can increase the fuel savings and the platooning rate significantly. We managed to increase it with a factor of nine despite having only 200–350 active HDVs on the network. The main results of the thesis indicate that it is possible to increase fuel savings noticeably with simple regional coordination schemes for vehicle platoons. / <p>QC 2010324</p>

Fast-Lipschitz optimization is a framework for optimization problems having a special structure in which the optimal solution is given by a set of equations, easily solvable through fixed point iterations. This solution method is simple to implement and particularly well suited for distributed optimization applications, such as those arising in wireless sensor networks. This thesis provides several theoretical contributions to the Fast-Lipschitz framework. In particular, it further develops the qualifying conditions under which a problem is guaranteed to be Fast-Lipschitz.Known qualifying conditions are unified and extended by a new set of conditions. This is done through a newapproach to the analysis of existing conditions, based on the Karush--Kuhn--Tucker (KKT) conditions.The Fast-Lipschitz framework is further extended by examining problem structures that are not treatable by the existing framework, such as problems with more or fewer constraints than variables. Finally, this thesis provides a comparison between the convergence properties of Fast-Lipschitz optimization and those of a traditional method based on gradient descent. The thesis then applies the theory of Fast-Lipschitz optimization to the area of radio power control over wireless networks.Conditions are given under which well known distributed power control algorithms,such as those involving standard types of interference functions, fall in the Fast-Lipschitz framework.This gives a direct connection between these algorithms and a family of optimization problems, and the fixed points thereby assume a meaning of optimality. Finally, the thesis gives illustrative examples of the new theory and examples of applications such as in a general non-convex optimal control problem and a non-monotonic power control problem. / <p>QC 20140826</p>

Multi-agent systems are interconnected control systems with many application domains. The first part of this thesis considers nonlinear multi-agent systems, where the control input can be decoupled into a product of a nonlinear gain function depending only on the agent's own state, and a nonlinear interaction function depending on the relative states of the agent's neighbors. We prove stability of the overall system, and explicitly characterize the equilibrium state for agents with both single- and double-integrator dynamics. Disturbances may seriously degrade the performance of multi-agent systems. Even constant disturbances will in general cause the agents to diverge, rather than to converge, for many control protocols. In the second part of this thesis we introduce distributed proportional-integral controllers to attenuate constant disturbances in multi-agent systems with first- and second-order dynamics. We derive explicit stability criteria based on the integral gain of the controllers. Lastly, this thesis presents both centralized and distributed frequency controllers for electrical power transmission systems. Based on the theory developed for multi-agent systems, a decentralized controller regulating the system frequencies under load changes is proposed. An optimal distributed frequency controller is also proposed, which in addition to regulating the frequencies to the nominal frequency, minimizes the cost of power generation. / <p>QC 20130221</p>

Many classical problems in system identification, such as the classical predictionerror method and regularized system identification, identification of Hammersteinand cascaded systems, blind system identification, as well as errors-in-variablesproblems and estimation with missing data, can be seen as particular instancesof the general problem of the identification of systems with limited information.In this thesis, we introduce a framework for the identification of linear dynamicalsystems subject to inputs that are not perfectly known. We present the class ofuncertain-input models—that is, linear systems subject to inputs about which onlylimited information is available. Using the Gaussian-process framework, we modelthe uncertain input as the realization of a Gaussian process. Similarly, we model theimpulse response of the linear system as the realization of a Gaussian process. Usingthe mean and covariance functions of the Gaussian processes, we can incorporateprior information about the system in the model. Interpreting the Gaussian processmodels as prior distributions of the unknowns, we can find the minimum mean-square-error estimates of the input and of the impulse response of the system. Theseestimates depend on some parameters, called hyperparameters, that need to beestimated from the available data. Using an empirical Bayes approach, we estimatethe hyperparameters from the marginal likelihood of the data. The maximizationof the marginal likelihood is carried out using an iterative scheme based on theExpectation-Maximization method. Depending on the assumptions made on themodels of the input and of the system, the standard E-step may not be available inclosed form. In this case, the E-step is replaced with a Markov Chain Monte Carlointegration scheme based on the Gibbs sampler. After showing how to estimate thesystem and the hyperparameters, we show how to specialize the general uncertain-input model to particular structures and how to modify the general estimationmethod to account for these particular structures. In the last chapter, we show inwhat sense the aforementioned classical system identification problems can be seenas uncertain-input model identification problems; we show the effectiveness of theframework in dealing with these classical problems in several numerical examples. / <p>QC 20160520</p>

The purpose of system identification is to build mathematical models for dynamical systems from experimental data. With the current increase in complexity of engineering systems, an important challenge is to develop accurate and computationally efficient algorithms. For estimation of parametric models, the prediction error method (PEM) is a benchmark in the field. When the noise is Gaussian and a quadratic cost function is used, PEM provides asymptotically efficient estimates if the model orders are correct. A disadvantage with PEM is that, in general, it requires minimizing a non-convex function. Alternative methods are then needed to provide initialization points for the optimization. Two important classes of such methods are subspace and instrumental variables. Other methods, such as Steiglitz-McBride, use iterative least squares to avoid the non-convexity of PEM. This thesis focuses on this class of methods, with the purpose of addressing common limitations in existing algorithms and suggesting more accurate and computationally efficient ones. In particular, the proposed methods first estimate a high order non-parametric model and then reduce this estimate to a model of lower order by iteratively applying least squares. Two methods are proposed. First, the weighted null-space fitting (WNSF) uses iterative weighted least squares to reduce the high order model to a parametric model of interest. Second, the model order reduction Steiglitz-McBride (MORSM) uses pre-filtering and Steiglitz-McBride to estimate a parametric model of the plant. The asymptotic properties of the methods are studied, which show that one iteration provides asymptotically efficient estimates. We also discuss two extensions for this type of methods: transient estimation and estimation of unstable systems. Simulation studies provide promising results regarding accuracy and convergence properties in comparison with PEM. / <p>QC 20160819</p>

The topic of the current thesis is the reduction of the average packet delay in two-hop wireless cooperative networks with buffer-aided relays. This type of networks is of particular interest since it constitutes the building block for extended networks with multiple hops and numerous relays. Back-pressure-like algorithms are developed for the HRS and max − link relay selection schemes. First, an algorithm is developed and applied for both the HRS and the max − link protocols. It reduces the average delay considerably, but, in the case of the max − link the diversity of the system is reduced resulting in higher outage probabilities. For this reason, a new algorithm is developed that aims at maintaining a high diversity throughout the operation of the network. Distributed implementations of the algorithms are also discussed. The performance of the proposed algorithms is illustrated via simulations.

Hidden Markov Models (HMMs) are popular tools for modeling discrete time series. Since the parameters of these models can be hard to derive analytically or directly measure, various algorithms are available for estimating these from observed data. The most common method, the Expectation-Maximization algorithm, su ers from problems with local minima and slow convergence. A spectral algorithm that has received considerable attention in the eld of machine learning claims to avoid these issues. This thesis implements and benchmarks said algorithm on various systems to see how well it performs. One of the concerns with the proposed spectral algorithm is that it cannot guarantee that the estimates are stochastically valid: it may recover negative or complex probabilities, due to an eigenvalue decomposition. Another approach to the HMM identication problem is to leverage results from Non- Negative Matrix Factorization (NNMF) theory. Inspired by an algorithm employing a Structured NNMF (SNNMF), assumptions are presented to guarantee that the factorization problem can be cast into a convex optimization problem. Three novel recursive algorithms are then derived for estimating the dynamics of an HMM when the sensor dynamics are known. These can be used in an online setting where time and/or computational resources are limited, since they only require the current estimate of the HMM parameters and the new observation. Numerical results for the algorithms are provided.

This thesis discusses self- and event-triggered control for collective motion of autonomous underwater vehicles, for which rules are derived and analysed. The purpose is to enable coordinated underwater motion for agents that can not update their knowledge of the states of the group while submerged. A selftriggered control strategy is studied in which a designated leader agent broadcasts the upcoming waypoints for all agents, based on their desired position relative the leader. A Lyapunov based event-triggered approach is also studied using a potential based control strategy. Stability and convergence problems are discussed as well as the suitability of the control strategies based on feasible inter-surfacing times for the agents. Simulations illustrate the characteristics of the control strategies.

We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical system. We present robust model-based methods based on convex optimization, which minimize the worst-case cost with respect to uncertainty around model estimates. To quantify uncertainty, we derive a methodbased on Bayesian inference, which is directly applicable to robust control synthesis.We focus on control policies that can be iteratively updated after sequentially collecting data. More specifically, we seek to design control policies that balance exploration (reducing model uncertainty) and exploitation (control of the system) when exploration must be safe (robust).First, we derive a robust controller to minimize the worst-case cost, with high probability, given the empirical observation of the system. This robust controller synthesis is then used to derive a robust dual controller, which updates its control policy after collecting data. An episode in which data is collected is called exploration, and the episode using an updated control policy is exploitation. The objective is to minimize the worst-case cost of the updated control policy, requiring that a given exploration budget constrains the worst-case cost during exploration.We look into robust dual control in both finite and infinite horizon settings. The main difference between the finite and infinite horizon settings is that the latter does not consider the length of the exploration and exploitation phase, but it rather approximates the cost using the infinite horizon cost. In the finite horizon setting, we discuss how different exploration lengths affect the trade-off between exploration and exploitation.Additionally, we derive methods that balance exploration and exploitation to minimize the cumulative worst-case cost for a fixed number of episodes. In this thesis, we refer to such a problem as robust reinforcement learning. Essentially, it is a robust dual controller aiming to minimize the cumulative worst-case cost, and that updates its control policy in each episode.Numerical experiments show that the proposed methods have better performance compared to existing state-of-the-art algorithms. Moreover, experiments also indicate that the exploration prioritizes the uncertainty reduction in the parameters that matter most for control. / <p>QC 20200904</p>