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  • Presentation: 2018-10-19 10:00 Gustav Dahlander, Stockholm
    Yu, Pian
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Resource- and Time-Constrained Control Synthesis for Multi-Agent Systems2018Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Multi-agent systems are employed for a group of agents to achieve coordinated tasks, in which distributed sensing, computing, communication and control are usually integrated with shared resources. Efficient usage of these resources is therefore an important issue. In addition, in applications such as robotics, a group of agents may encounter the request of a sequence of tasks and deadline constraint on the completion of each task is a common requirement. Thus, the integration of multi-agent task scheduling and control synthesis is of great practical interest. In this thesis, we study control of multi-agent systems under a networked control system framework. The first purpose is to design resource-efficient communication and control strategies to solve consensus problem for multi-agent systems.The second purpose is to jointly schedule task sequence and design controllers for multiagent systems that are subject to a sequence of deadline-constrained tasks. In the first part, a distributed asynchronous event-triggered communication and control strategy is proposed to tackle multi-agent consensus. It is shown that the proposed event-triggered communication and control strategy fulfils the reduction of both the rates of sensor-controller communication and controller-actuator communication as well as excluding Zeno behavior. To further relax the requirement of continuous sensing and computing, a periodic event-triggered communication and control strategy is proposed in the second part. In addition, an observer-based encoder-decoder with finite-level quantizeris designed to deal with the constraint of limited data rate. An explicit formula for the maximum allowable sampling period is derived first. Then, it is proven that exponential consensus can be achieved in the presence of data rate constraint. Finally, in the third part, the problem of deadline-constrained multi-agent task scheduling and control synthesis is addressed. A dynamic scheduling strategy is proposed and a distributed hybrid control law is designed for each agent that guarantees the completion and deadline satisfaction of each task. The effectiveness of the theoretical results in the thesis is verified by several simulation examples.

    The full text will be freely available from 2018-10-19 13:00
  • Presentation: 2018-10-26 09:00 L 1, Stockholm
    Murekatete, Rachel Mundeli
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    An Analysis of Consequences of Land Evaluation and Path Optimization2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Planners who are involved in locational decision making often use raster-based geographic information systems (GIS) to quantify the value of land in terms of suitability or cost for a certain use. From a computational point of view, this process can be seen as a transformation of one or more sets of values associated with a grid of cells into another set of such values through a function reflecting one or more criteria. While it is generally anticipated that different transformations lead to different ‘best’ locations, little has been known on how such differences arise (or do not arise). Examples of such spatial decision problems can be easily found in the literature and many of them concern the selection of a set of cells (to which the land use under consideration is allocated) from a raster surface of suitability or cost depending on context. To facilitate GIS’s algorithmic approach, it is often assumed that the quality of the set of cells can be evaluated as a whole by the sum of their cell values. The validity of this assumption must be questioned, however, if those values are measured on a scale that does not permit arithmetic operations. Ordinal scale of measurement in Stevens’s typology is one such example. A question naturally arises: is there a more mathematically sound and consistent approach to evaluating the quality of a path when the quality of each cell of the given grid is measured on an ordinal scale? The thesis attempts to answer the questions highlighted above in the context of path planning through a series of computational experiments using a number of random landscape grids with a variety of spatial and non-spatial structures. In the first set of experiments, we generated least-cost paths on a number of cost grids transformed from the landscape grids using a variety of transformation parameters and analyzed the locations and (weighted) lengths of those paths. Results show that the same pair of terminal cells may well be connected by different least-cost paths on different cost grids though derived from the same landscape grid and that the variation among those paths is affected by how given values are distributed in the landscape grid as well as by how derived values are distributed in the cost grids. Most significantly, the variation tends to be smaller when the landscape grid contains more distinct patches of cells potentially attracting or distracting cost-saving passage or when the cost grid contains a smaller number of low-cost cells. The second set of experiments aims to compare two optimization models, minisum and minimax (or maximin) path models, which aggregate the values of the cells associated with a path using the sum function and the maximum (or minimum) function, respectively. Results suggest that the minisum path model is effective if the path search can be translated into the conventional least-cost path problem, which aims to find a path with the minimum cost-weighted length between two terminuses on a ratio-scaled raster cost surface, but the minimax (or maximin) path model is mathematically sounder if the cost values are measured on an ordinal scale and practically useful if the problem is concerned not with the minimization of cost but with the maximization of some desirable condition such as suitability.

  • Presentation: 2018-10-30 10:00 Q34, Stockholm
    Majal, Ghulam
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Marcus Wallenberg Laboratory MWL.
    On the Agglomeration of Particles in Exhaust Gases2018Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Particulate emissions from road transportation are known to have an adverse impact on human health as well the environment. As the effects become more palpable, stricter legislation have been proposed by regulating bodies. This puts forward a challenge for the automotive industry to develop after treatment technologies to fulfil the progressively stricter legislation. At present, the most common after-treatment technologies used for particulates are the diesel and gasoline particulate filters. The typical size distribution of the particles is such that the smallest particles in terms of size are in numbers the largest, although they are not influencing the total particle mass significantly. The most recent legislation have included restrictions on the particle number as well as particle mass. In this thesis numerical tools for studying the transport and interaction of particles in an exhaust flow are evaluated. The specific application is particle agglomeration as a mean to reduce the number of particles and manipulate the size distribution. As particles agglomerate the particle number distribution is shifted and larger sized agglomerates of particles are created reducing the number of ultra-fine particles. The particle agglomeration is obtained by forcing sudden acceleration and deceleration of the host gas carrying the particles by variations in the cross sectional areas of the geometry it is passing through. Initially, a simplified one dimensional model is utilized to assess the governing parameters of particle grouping. Grouping here means that the particles form and are transported in groups, thus increasing the probability for agglomeration. The lessons learned from the 1D-model are also used to design the three dimensional geometry: an axisymmetric corrugated pipe. Two different geometries are studied, they both have the same main pipe diameter but different diameter on the corrugations. The purpose is to find the potential onset of flow instabilities and the influence of 3D-effects such as recirculation on the agglomeration. The CFD simulations are performed using DES methodology. First the simulations are run without particles in a non pulsatile flow scenario. Later particles are added to the setup in a one way coupled approach (no particle-particle interaction). The main results were: 1) An additional criterion for grouping to the ones given in previous work on the 1D model is proposed. It is found that grouping is more likely if the combination of the pulse frequency and geometric wavelength is large. Furthermore, smooth pulse forms (modelling the modulation in the flow due to the geometry) yielded more grouping than other more abrupt pulse shapes. However, idealised inlet pulses underestimate the extent of grouping compared to actual engine pulses. 2) For the geometry with larger maximum cross sectional area stronger flow separation was observed along with higher turbulent kinetic energy. 3) Particles were added in the flow field and a reduction in the particle count was observed in the initial simulations for particles going from the first corrugated segment to the last. Natural extensions of the present work would be to consider pulsatile flow scenarios, particle-particle interaction and a polydisperse setup for the particles

  • Presentation: 2018-11-09 10:00 M311, Stockholm
    Szipka, Károly
    KTH, School of Industrial Engineering and Management (ITM).
    Modelling and Management of Uncertainty in Production Systems: from Measurement to Decision2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.

  • Presentation: 2018-11-12 10:00 V32, Stockholm
    Gao, Yulong
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Stochastic Invariance and Aperiodic Control for Uncertain Constrained Systems2018Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Uncertainties and constraints are present in most control systems. For example, robot motion planning and building climate regulation can be modeled as uncertain constrained systems. In this thesis, we develop mathematical and computational tools to analyze and synthesize controllers for such systems.

    As our first contribution, we characterize when a set is a probabilistic controlled invariant set and we develop tools to compute such sets. A probabilistic controlled invariantset is a set within which the controller is able to keep the system state with a certainprobability. It is a natural complement to the existing notion of robust controlled invariantsets. We provide iterative algorithms to compute a probabilistic controlled invariantset within a given set based on stochastic backward reachability. We prove that thesealgorithms are computationally tractable and converge in a finite number of iterations. The computational tools are demonstrated on examples of motion planning, climate regulation, and model predictive control.

    As our second contribution, we address the control design problem for uncertain constrained systems with aperiodic sensing and actuation. Firstly, we propose a stochastic self-triggered model predictive control algorithm for linear systems subject to exogenous disturbances and probabilistic constraints. We prove that probabilistic constraint satisfaction, recursive feasibility, and closed-loop stability can be guaranteed. The control algorithm is computationally tractable as we are able to reformulate the problem into a quadratic program. Secondly, we develop a robust self-triggered control algorithm for time-varying and uncertain systems with constraints based on reachability analysis. In the particular case when there is no uncertainty, the design leads to a control system requiring minimum number of samples over finite time horizon. Furthermore, when the plant is linear and the constraints are polyhedral, we prove that the previous algorithms can be reformulated as mixed integer linear programs. The method is applied to a motion planning problem with temporal constraints.