Filetype
Parallel robots are generally said to be more accurate but to have a smaller workspace compared to serial robots. With the Gantry-Tau robot, a parallel robot with a large, reconfigurable workspace was presented. This article tries to identify the maximum achievable end-effector positioning accuracy of the Gantry-Tau robot. To this end, a couple of new kinematic error models are presented and evalu
Theory for Distributed Model Predictive Control (DMPC) is developed based on dual decomposition of the convex optimization problem that is solved in each time sample. The process to be controlled is an interconnection of several subsystems, where each subsystem corresponds to a node in a graph. We present a stopping criterion for the DMPC scheme that can be locally verified by each node and that g
We approach the simultaneous localization and mapping problem by using an ultrasound sensor and wheel encoders on a mobile robot. The measurements are modeled to yield a conditionally linear model for all the map states. Moreover, we implement a Rao-Blackwellized particle smoother (RBPS) for jointly estimating the position of the robot and the map. The method is applied and successfully verified b
A resource management framework for media applications modelled as dataflow streams is described. The framework is aimed at multi-core applications and the main resource managed is the CPU time. The management is implemented using reservation-based scheduling in combination with optimization and feedback. Experiences of applying the framework to media applications are reported. A simulation model
During the recent years, researchers in control engineering have proposed more resource-efficient control strategies than the traditional periodic control paradigm, resulting in two main control approaches: event-based and self-triggered control. Such nonperiodic, state-based control methods, although subject to many open research problems, can provide similar control performance as periodic contr
We consider a class of distributed algorithms for computing arithmetic averages (average consensus) over net- works of agents connected through digital noisy broadcast channels. Our algorithms combine error-correcting codes with the classical linear consensus iterative algorithm, and do not require the agents to have knowledge of the global network structure. We improve the performance by introduc
The adaptive resource management framework implemented in the European ACTORS project is presented. A resource manager has been developed that collaborates with a new Linux scheduler providing support for hard constant bandwidth server reservations, in order to adapt applications to changes in resource availability and to adapt the resource allocation to changes in application requirements. The fo
Self-adaptation enables software to execute successfully in dynamic, unpredictable, and uncertain environments. Control theory provides a broad set of mathematically grounded techniques for adapting the behavior of dynamic systems. While it has been applied to specific software control problems, it has proved difficult to define methodologies allowing non-experts to systematically apply control te
Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes -expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities
In the study of distributed systems, it is of fundamental interest to understand how specifications on local components and interconnections influence global properties of the system. In this paper, we consider linear continuous time systems described by sparse matrices. Properties of interest are stability and quadratic performance specifications such as passivity and input-output gain. In partic
It has been shown that optimal controller synthesis for positive systems can be formulated as a linear program. Leveraging these results, we propose a scalable iterative algorithm for the systematic design of sparse, small gain feedback strategies that stabilize the evolutionary dynamics of a generic disease model. We achieve the desired feedback structure by augmenting the optimization problems w
For interconnected systems and systems of large size, aggregating information of subsystems studied individually is useful for addressing the overall stability. In the Lyapunov- based analysis, summation and maximization of separately constructed functions are two typical approaches in such a philosophy. This paper focuses on monotone systems which are common in control applications and elucidates
This paper presents a reinforcement learning algorithm and provides conditions for global convergence to Nash equilibria. For several reinforcement learning schemes, including the ones proposed here, excluding convergence to action profiles which are not Nash equilibria may not be trivial, unless the step-size sequence is appropriately tailored to the specifics of the game. In this paper, we sides
Managing the resources of a virtualized data-center is a key issue in cloud computing. Existing research mostly assumes that applications are either allocated the required resources or fail. Combined with the fact that most cloud applications have dynamic resource requirements, this imposes a fundamental limitation to cloud computing: To guarantee on-demand resource allocations, the data-center ne
