An iterative Newton\u27s method for output-feedback LQR design for large-scale systems with guaranteed convergence

The paper proposes a novel iterative output-feedback control design procedure, with necessary and sufficient stability conditions, for linear time-invariant systems within the linear quadratic regulator (LQR) framework. The proposed iterative method has a guaranteed convergence from an initial Lyapunov matrix, obtained for any stabilizing state-feedback gain, to a stabilizing output-feedback solution. Another contribution of the proposed method is that it is computationally much more tractable then algorithms in the literature, since it solves only a Lyapunov equation at each iteration step. Therefore, the proposed algorithm succeed in high dimensional problems where other, state-of-the-art methods fails. Finally, numerical examples illustrate the effectiveness of the proposed method

A brief history of the Italian marine biology

This paper is a short history of Italian marine biology, starting from the mid 16th century. During the Renaissance, a profound curiosity for marine sciences animated the scientific thought and several Italian naturalists started to collect rare and unusual marine items, sometimes acting with little critical sense towards medieval unbelievable legends. The 17th and 18th centuries saw a development of botany and zoology as modern disciplines and Italian scholars started to study the Mediterranean fauna and flora. They became active mainly at the Universities of Trieste, Venice, Palermo, Naples, Rome and Genoa and in other scientific institutions that arose under the different political regimes in which Italy was divided at that time. The Kingdom of Italy, born in 1861 with enormous financial difficulties, was interested in reaching an international scientific limelight: hence, some oceanographic expeditions were organized all around the world with a significant collection of data and specimens. The scientific interest for sea life increased and became at international level at the end of the 19th century, with the foundations of the first shore-based Zoological Stations in Trieste and Naples. At the beginning of the 20th century, intensive studies of inshore benthic communities by dredging and, afterwards by diving, started concurrently with those on structure and dynamics of plankton and fish populations which yielded a significant knowledge of the marine life from the Mediterranean continental platform. After the Second World War, the fundamental studies conducted at the Zoological Station of Naples on genetics, embryology and developmental biology using marine organisms as study models, were spread to different universities, going to constitute an Italian school of experimental embryology of international value. Today, the modern Italian marine biology is increasingly multi-disciplinary, requiring the participation of biochemists, geneticists and mathematicians and it opens up to new frontiers often linked to the global changes

Reinforcement learning for condition-based control of gas turbine engines

A condition-based control framework is proposed for gas turbine engines using reinforcement learning and adaptive dynamic programming (RL-ADP). The system behaviour, specifically the fuel efficiency function and constraints, exhibit unknown degradation patterns which vary from engine to engine. Due to these variations, accurate system models to describe the true system states over the life of the engines are difficult to obtain. Consequently, model-based control techniques are unable to fully compensate for the effects of the variations on the system performance. The proposed RL-ADP control framework is based on Q-learning and uses measurements of desired performance quantities as reward signals to learn and adapt the system efficiency maps. This is achieved without knowledge of the system variation or degradation dynamics, thus providing a through life adaptation strategy that delivers improved system performance. In order to overcome the long standing difficulties associated with the application of adaptive techniques in a safety critical setting, a dual-control loop structure is proposed in the implementation of the RL-ADP scheme. The overall control framework maintains guarantees on the main thrust control loop whilst extracting improved performance as the engine degrades by tuning sets of variable geometry components in the RL-ADP control loop. Simulation results on representative engine data sets demonstrate the effectiveness of this approach as compared to an industry standard gain scheduling

Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles

Human-Robot Collaboration for Kinesthetic Teaching

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human–robot collaboration (HRC) in industrial settings, as an attempt to increase flexibility in manufacturing applications by incorporating human intelligence and dexterity to these processes. This thesis presents methods for improving the involvement of human operators in industrial settings where robots are present, with a particular focus on kinesthetic teaching, i.e., manually guiding the robot to define or correct its motion, since it can facilitate non-expert robot programming.To increase flexibility in the manufacturing industry implies a loss of a fixed structure of the industrial environment, which increases the uncertainties in the shared workspace between humans and robots. Two methods have been proposed in this thesis to mitigate such uncertainty. First, null-space motion was used to increase the accuracy of kinesthetic teaching by reducing the joint static friction, or stiction, without altering the execution of the robotic task. This was possible since robots used in HRC, i.e., collaborative robots, are often designed with additional degrees of freedom (DOFs) for a greater dexterity. Second, to perform effective corrections of the motion of the robot through kinesthetic teaching in partially-unknown industrial environments, a fast identification of the source of robot–environment contact is necessary. Fast contact detection and classification methods in literature were evaluated, extended, and modified to use them in kinesthetic teaching applications for an assembly task. For this, collaborative robots that are made compliant with respect to their external forces/torques (as an active safety mechanism) were used, and only embedded sensors of the robot were considered.Moreover, safety is a major concern when robotic motion occurs in an inherently uncertain scenario, especially if humans are present. Therefore, an online variation of the compliant behavior of the robot during its manual guidance by a human operator was proposed to avoid undesired parts of the workspace of the robot. The proposed method used safety control barrier functions (SCBFs) that considered the rigid-body dynamics of the robot, and the method’s stability was guaranteed using a passivity-based energy-storage formulation that includes a strict Lyapunov function.All presented methods were tested experimentally on a real collaborative robot

Challenges and Possibilities of Overtaking Strategies for Autonomous Vehicles

This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes

Energy management and peer-to-peer trading in future smart grids: a distributed game-theoretic approach

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We consider the economic dispatch problem for a day-ahead, peer-to-peer (P2P) electricity market of prosumers (i.e., energy consumers who can also produce electricity) in a distribution network. In our model, each prosumer has the capability of producing power through its dispatchable or non-dispatchable generation units and/or has a storage energy unit. Furthermore, we consider a hybrid main grid & P2P market in which each prosumer can trade power both with the main grid and with (some of) the other prosumers. First, we cast the economic dispatch problem as a noncooperative game with coupling constraints. Then, we design a fully-scalable algorithm to steer the system to a generalized Nash equilibrium (GNE). Finally, we show through numerical studies that the proposed methodology has the potential to ensure safe and efficient operation of the power grid.This work was partially supported by NWO under research projects OMEGA (grant n. 613.001.702), P2P-TALES (grant n. 647.003.003), the ERC under research project COS-MOS (802348), the European Union’s Horizon 2020 research and innovationprogramme under the Marie Skłodowska-Curie grant agreement No 675318 (INCITE)Peer ReviewedPostprint (author's final draft