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We investigate optimal maneuvers for vehicles on different road surfaces such as asphalt, snow, and ice. This study is motivated by the desire to find control strategies for improved future vehicle safety and driver assistance technologies. Based on earlier presented empirical measurements for tire-force characteristics, we develop vehicle and tire models corresponding to different road conditions
A procedure for guaranteed cost control design of delayed linear bilateral teleoperation systems with nonlinear external forces is proposed. The assumption that the external forces are nonlinear functions of velocities and/or positions of local devices, and one part of these forces satisfies a sector condition has been made. A virtual tool system is introduced to 'observe' the forces at the remote
Manipulation tasks are characterized by continuous motion trajectories containing a set of key phases. In this paper, we propose a probabilistic method to autonomously segment the motion trajectories for estimating the key phases embedded in such a task. The autonomous segmentation process relies on principal component analysis to adaptively project into one of the low-dimensional subspaces, in wh
Partially Premixed Combustion (PPC) is an internal combustion engine concept that aims to yield low NOx and soot emission levels together with high engine efficiency. PPC belongs to the class of low temperature combustion concepts where the ignition delay is prolonged in order to promote the air-fuel-mixture homogeneity in the combustion chamber at the start of combustion. A more homogeneous combu
We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategic-form game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a limited set of neighbors. Agents trade off the benefit from links, which is determined by a distance-
Modern industrial robots are traditionally programmed to follow desired trajectories, with the only feedback coming from the internal position/angle sensors in the joints. The robots are in general very accurate in tracking the desired motion, and they have become indispensable in many applications, such as spot welding and painting in the automotive industry. In more complex tasks, such as physic
In this paper a controller reduction method which preserves closed loop stability is described. A Lyapunov inequality based sufficient condition is proposed in the search of the reduced controller. The reduced controller leads to a stable closed loop system with guaranteed approximation quality. Furthermore, the proposed problem can be formulated as a matrix approximation problem which can be solv
A widespread and flexible use of robots in rapidly changing working environments could be greatly enhanced by human-robot interaction and collaboration. Humans and robots have complementary skills. The robotic worker can relieve the human from repetitive work, and the human can make robot deployment easier by managing nonstandard or particularly skilful operations. Such a scenario, however, requir
Composition is a practice of key importance in software engineering. When real-time applications are composed, it is necessary that their timing properties (such as meeting the deadlines) are guaranteed. The composition is performed by establishing an interface between the application and the physical platform. Such an interface typically contains information about the amount of computing capacity
It has long been recognized that one of the fundamental difficulties in the estimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal
The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of methods such as particle filtering and smoothing. There are three areas of contributions: software implementation, applications of nonlinear estimation and some theoretical extensions to existing algorithms. The common theme for all the work presented is the pyParticleEst software framework, which has b
The thesis discusses closed-loop control of the hypnotic and the analgesic components of anesthesia. The objective of the work has been to develop a system which independently controls the intravenous infusion rates of the hypnotic drug propofol and analgesic drug remifentanil. The system is designed to track a reference hypnotic depth level, while maintaining adequate analgesia. This is complicat
We consider model predictive control (MPC) problems with linear dynamics, polytopic constraints, and quadratic objective. The resulting optimization problem is solved by applying an accelerated gradient method to the dual problem. The focus of this paper is to provide bounds on the number of iterations needed in the algorithm to guarantee a prespecified accuracy of the dual function value and the
This manuscript proposes a novel viewpoint on computing systems' modelling. The classical approach is to consider fully functional systems and model them, aiming at closing some external loops to optimise their behaviour. On the contrary, we only model strictly physical phenomena, and realise the rest of the system as a set of controllers. Such an approach permits rigorous assessment of the obtain
Adaptive reservation is a real-time scheduling technique in which each application is associated a fraction of the computational resource (a reservation) that can be dynamically adapted to the varying requirements of the application by using appropriate feedback control algorithms. An adaptive reservation is typically implemented by using an aperiodic server (e.g. sporadic server) algorithm with f
A parametrized model in addition to the control and state-space variables depends on time-independent design parameters, which essentially define a family of models. The goal of parametrized model reduction is to approximate this family of models. In this paper, a reduction method for linear time-invariant (LTI) parametrized models is presented, which constitutes the development of a recently prop
