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We present a framework for the solution of control and estimation problems under a signal-to-noise (SNR) ratio constraint. The framework can be used to design optimal linear controllers, based on output feedback, with two degrees of freedom: One part of the controller is placed before the communication channel and represents sensing and encoding operations. The other part represents decoding and i
Joint wheel-slip and vehicle-motion estimation is considered, based on measurements from wheel encoders, an inertial measurement unit, and a global positioning system (GPS). The proposed strategy effectively employs the Rao-Blackwellized particle-filtering framework using a kinematic model. Key vari- ables in active safety systems, such as longitudinal velocity, roll angle, and wheel slip for all
This paper presents a scheme to construct distributed observers for a system consisting of agents interconnected in a graph structure. The scheme is an iterative procedure to improve the observers with respect to global performance. It is modular in the sense that each agent iterates using only local model information. As a consequence, the complexity of the scheme scales linearly with the size of
In production industry today a lot of engineering time is required to develop and maintain control applications. One part of the control applications are the state machines which typically are written in Grafcet/SFC. Several efforts have been made to extend Grafcet/SFC to achieve more effective and convenient development, e.g. by introducing hierarchical structuring, reusable sub-state machines, a
Parallel robots are generally said to be more accurate and have a smaller workspace compared to serial robots. With the Gantry-Tau robot, a parallel robot with relatively large workspace was presented. This article examines the end-effector positioning accuracy of the Gantry-Tau robot. End-effector positioning errors arising from joint positioning or modeling errors are studied. A geometric approa
A general framework for the study of robust synchronisation in large-scale networks is provided. Agents are represented as a common nominal linear time-invariant (LTI) single-input-single-output (SISO) system with simple poles on the imaginary axis, subject to LTI SISO stable multiplicative perturbations. The agents exchange information in order to achieve output-synchronisation, namely steer thei
Although the research on the design of heterogeneous concurrent systems has a long and rich history, a unified design methodology and tool support have not emerged so far. Therefore, the creation of such systems remains a difficult, time-consuming and error-prone process. The absence of principled support for system evaluation and optimization at high level of abstraction makes the quality of the
This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve
Gradient orientations are a common feature used in many computer vision algorithms. It is a good feature when the gradient magnitudes are high, but can be very noisy when the magnitudes are low. This means that some gradient orientations are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those with higher uncertainty.
We analyze reinforcement learning under so-called "dynamic reinforcement." In reinforcement learning, each agent repeatedly interacts with an unknown environment (i.e., other agents), receives a reward, and updates the probabilities of its next action based on its own previous actions and received rewards. Unlike standard reinforcement learning, dynamic reinforcement uses a combination of long-ter
Mathematical optimization is becoming increasingly important for engineering in general and control in particular. This thesis deals with numerical methods, primarily direct collocation, and symbolic methods, primarily block-triangular ordering and tearing, for numerical solution of general dynamic optimization problems involving dynamical systems modeled by large-scale differential-algebraic equa
We present the PalCom Object Notation, a textual data representation format for communication between internet of things which support binary and textual data. The format does not require parsing of user data (or the “payload”) and is thus efficient to use also for large binary values such as digital images, audio and video as well as for short textual values. These can be mixed in the same messag
We propose a novel approach to the problem of inverse kinematics for possibly redundant planar manipulators. We show that, by considering the joints as point masses in a fictitious gravity field, and by adding proper constraints to take into account the length of the links, the kinematic inversion may be cast as a convex programming problem. Convex constraints in the decision variables (in particu
