Lyapunov Self-triggered Controller for Nonlinear Trajectory Tracking of Unicycle-type Robot

This paper focuses on the design and implementation of an aperiodic control of nonholonomic robots tracking nonlinear trajectories. The main objective of our controller is to reduce the number of updates while preserving control performance guarantees. To solve the problem in a more efficient way, we design two aperiodic control solutions, one to reach a target point and a second to track a predefined nonlinear trajectory. Unlike most previous work, our triggering condition only updates the controller when the time derivative of the Lyapunov function becomes nonnegative, without taking into account the measurement error. Multiple simulated results with different initial conditions are included, showing how our control solution significantly reduces the need for communication in comparison with periodic and other aperiodic strategies while preserving a desired tracking performance. To validate the proposal experimental tests of each control technique with a P3-DX robot remotely controlled through an IEEE 802.11g wireless network are also carried out

An experimental strategy for characterizing inductive electromagnetic energy harvesters

Condition monitoring of high voltage power lines through self-powered sensor systems has become a priority for utilities with the aim of detecting potential problems, enhancing reliabilityof the power transmission and distribution networks and mitigating the adverse impact of faults. Energy harvesting from the magnetic field generated by the alternating current flowing through highvoltage lines can supply the monitoring systems with the required power to operate without relying onhard-wiring or battery-based approaches. However, developing an energy harvester, which scavengesthe power from such a limited source of energy, requires detailed design considerations, which maynot result in a technically and economically optimal solution. This paper presents an innovativesimulation-based strategy to characterize an inductive electromagnetic energy harvester and the power conditioning system. Performance requirements in terms of the harvested power and output voltage range, or level of magnetic core saturation can be imposed. Different harvester configurations, whichsatisfy the requirements, have been produced by the simulation models. The accuracy and efficiency ofthis approach is verified with an experimental setup based on an energy harvester, which consists ofa Si-steel magnetic core and a power conditioning unit. For the worst-case scenario with a primary current of 5 A, the maximum power extracted by the harvester can be as close as 165 mW, resulting ina power density of 2.79 mW/cm3.Comunidad de MadridAgencia Estatal de Investigació

A day-ahead irradiance forecasting strategy for the integration of photovoltaic systems in virtual power plants

Encouraged by the considerable cost reduction, small-scale solar power deployment has become a reality during the last decade. However, grid integration of small-scale photovoltaic (PV) solar systems still remains unresolved. High penetration of Renewable Energy Sources (RESs) results in technical challenges for grid operators. To address this, Virtual Power Plants (VPPs) have been defined and developed to manage distributed energy resources with the aim of facilitating the integration of RESs. This paper introduces a hybrid irradiance forecasting approach aimed at facilitating the integration of PV systems into a VPP, especially when a historical irradiance dataset is exiguous or non-existent. This approach is based on Artificial Neural Networks (ANNs) and a novel similar hour-based selection algorithm, has been tested for a real PV installation, and has been validated also considering irradiance measurements from an aggregation of ground-based meteorological stations, which emulate the nodes of a VPP. Under a reduced historical dataset, the results show that the proposed similar hour-based method produces the best forecasts with regard to those obtained by the ANN-based approach. This is particularly true for one-month and two-month datasets minimizing the mean error by 16.32% and 9.07% respectively. Finally, to demonstrate the potential of the proposed approach, a comparative analysis has been carried out between the hybrid method and the most used benchmarks in the literature, namely, the persistence method and the method based on similar days. It has been demonstrated conclusively that the proposed model yields promising results regardless the length of the historical dataset.Comunidad de MadridAgencia Estatal de Investigació

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.Comunidad de Madri

A realistic evaluation of indoor robot position tracking systems: The IPIN 2016 competition experience

We report a novel open competition aimed at evaluating accurate robot position tracking in indoor environments. The competition was organized within the IPIN 2016 (Indoor Positioning and Indoor Navigation international Conference). Here, we describe the competition, the competitors and their final results. The challenges of this new competition included: tracking an industrial robot following an unknown path but with a defined ground-truth, and open positioning system to be deployed on-site, with no restrictions apart from those related to safety issues. Our aim here is to provide sufficient detail to serve as a solid basis for future competition initiatives with a similar scope, using common metrics and objective evaluation procedures. In addition, the real systems evaluated represent state-of-the-art performance, and thus offer interesting solutions to the problem posed in the competition.Agencia Estatal de InvestigaciónUniversidad de Alcal