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We consider robust output feedback distributed model predictive control (DMPC). The proposed controller is based on the results in [8] in which nominal stability and feasibility was proven for a DMPC-formulation without terminal constraint set or terminal cost in the optimization. We extend these results to show robust stability under state feedback as well as output feedback when dynamics and mea
This thesis presents models and methods for feedback-based resource management for cyber-physical systems. Common for the scenarios considered are severe resource constraints, uncertain and time-varying conditions and the goal of enabling flexibility in systems design rather than restricting it. A brief survey on reservation-based scheduling, an important enabling technology for this thesis, is pr
A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermor
This thesis investigates the combination of pose and radio channel estimation. Pose is the knowledge of the position and orientation of a device whereas the radio channel describes the transmission medium between radio transmitters and receivers. The two subjects are both active research topics with a long history of applications but there has to the author's knowledge been very little work publis
In this paper, we revisit the problem of estimating the trifocal tensor from image line measurements. With measurements of corresponding lines in three views, a linear method [1] requiring 13 lines was developed to estimate the trifocal tensor from which projective reconstruction of the scene is made possible. By further utilizing the nonlinear constraints on the trifocal tensor, we propose severa
Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive contro
Imaging and inverse scattering of seismic reflection data can be formulated in terms of a certain class of Fourier integral operators. We present an approximation and discretization of such operators following a multiscale approach, based on wave packets as the quanta for representing seismic data. One of the key ingredients in our approach is the coupling of dyadic parabolic decomposition and pro
The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding aplications are needed. Unfortunately existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to efficient CPU allocation for ti
We present the open-source software framework in JModelica.org for numerically solving large-scale dynamic optimization problems. The framework solves problems whose dynamic systems are described in Modelica, an open modeling language supported by several different tools. The framework implements a numerical method based on direct local collocation, of which the details are presented. The implemen
We consider the problem of gray-box identification of dynamic models for mechanical systems. In particular, the problem is approached by means of continuous-time system identification using subspace-based methods based on discrete-time input-output identification data. A method is developed, with the property that the structure of the model resulting from fundamental physical first principles is o
Abstract in UndeterminedMinimum distances and maximum likelihood error probabilities of serial turbo codes with uniform interleaver are analyzed. It is shown that, for a fraction of interleavers approaching one as the block-length grows large, the minimum distance of serial turbo codes grows as a positive power of their block-length, while their error probability decreases exponentially fast in so
Industrial robots are usually programmed to follow desired trajectories, and are very good at position-controlled tasks. New applications, however, often require physical contact between the robot and its environment, and then the position control accuracy is generally not sufficient. Force control is a suitable alternative. The environment is often stiff, and then it is crucial to design appropri
A major challenge for a person with diabetes is to adapt insulin dosage regimens and food intake to keep blood glucose within tolerable limits during daily life activities. The early knowledge about the effects of inputs on glycemia would provide the patients with invaluable information for appropriate on-the-spot decision making concerning the management of the disease. Against this background, i
Mapping coastal inlets and their catchment areas is essential for management of the coastal zone. The coastal inlets are important channels of exchange of nutrients, water and sediment between the land and sea. They are also important elements of the coastal hydrological system and play a vital role in controlling the water flow into the sea during floods. Blocking of the coastal inlets is one of
Abstract in UndeterminedRobustness of distributed routing policies is studied for dynamical networks, with respect to adversarial disturbances that reduce the link flow capacities. A dynamical network is modeled as a system of ordinary differential equations derived from mass conservation laws on a directed acyclic graph with a single origin-destination pair and a constant total outflow at the ori
The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, depends on the conditioning of the optimization problem data. This conditioning is related to a metric that is defined by the space on which the optimization problem is stated; selecting a space on which the optimization data is better conditioned improves the performance of the algorithm. In this p
