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The objective of this paper is to explain novel sustainable robotics solutions for cities. Those new proposals appear under the ECHORD++ project which is a good tool to meet academia and industry with the objective of providing innovative technological solutions. In this paper, authors explain the tool as well as the methodology to promote robotics research in urban environments, and the on-going experience will demonstrate that huge advances are made in this field. Peer Reviewed

Product-Service Systems (PSS), if properly designed and implemented, represent a promising approach to sustainability. However, there is a number of organisational, cultural and regulatory barriers that hinder the widespread PSS implementation. In this paper, the authors investigated Distributed Manufacturing (DM) as a promising production model which can be applied to PSS to address some of its implementation barriers and improve its sustainability. To that end, existing PSS implementation barriers were collected and coupled with systematically analysed favourable DM features to describe a set of PSS+DM near-future scenarios, addressing the complete PSS lifecycle. Scenarios were then integrated into the PSS+DM Design Tool aiming to support idea generation for PSS implementation. The tool was tested with students, PSS and/or DM experts, manufacturing companies and design practitioners through two rounds of workshops in order to evaluate its completeness, effectiveness and usability and define recommendations for improvements. Based on the results, the improved final version of the PSS+DM Design Tool was developed, presenting the potential to support idea generation to improve sustainable PSS implementation through integrating DM features in each PSS lifecycle stage.

Microgrids are increasingly recognized as a key technology for the integration of distributed energy resources into the power network, allowing local clusters of load and distributed energy resources to operate autonomously. However, microgrid operation brings new challenges, especially in islanded operation as frequency and voltage control are no longer provided by large rotating machines. Instead, the power converters in the microgrid must coordinate to regulate the frequency and voltage and ensure stability. We consider the problem of designing controllers to achieve these objectives. Using passivity theory to derive decentralized stability conditions for the microgrid, we propose a control design method for grid-forming inverters. For the analysis we use higher-order models for the inverters and also advanced dynamic models for the lines with an arbitrarily large number of states. By satisfying the decentralized condition formulated, plug-and-play operation can be achieved with guaranteed stability, and performance can also be improved by incorporating this condition as a constraint in corresponding optimization problems formulated. In addition, our control design can improve the power sharing properties of the microgrid compared to previous non-droop approaches. Finally, realistic simulations confirm that the controller design improves the stability and performance of the power network. 14 pages, 10 figures

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict topology transitions to adapt their trajectories while optimizing their goals. Moreover, conditions to guarantee recursive feasibility and robust stability of the system are provided. Finally, the proposed control method is tested via a simulated eight-coupled tanks plant. Ministerio de Ciencia e Innovación FPU18{04476 Ministerio de Economía DPI2017-86918-R Ministerio de Economía DPI2015-67341-C02-01 Unión Europea No. 789051

A voyage optimization algorithm is an essential component in a ship’s routing concerning safety, energy efficiency, arrival punctuality, etc. In this study, predictive optimization is integrated with an Isochrone-based voyage optimization algorithm for energy-efficient sailing. Different waypoints generation and grid partition strategies in search spaces are proposed to achieve smooth convergence toward the destination, and costs ahead of the current sailing time stages are estimated in the cost function to avoid the local suboptimization. Based on these measures, this paper introduces the Isochrone-based predictive optimization (IPO) method that can achieve enhanced and robust performance in real-time multi-objective voyage optimization. The unrealistic routes with abrupt turns that occur in the traditional Isochrone and graph search methods are avoided. The IPO method can suggest energy-efficient routes in diverse sailing environments, while complying with punctuality requirements in voyage planning. Meanwhile, it requires a few computational resources that enable online and real-time adjustment during voyage execution, adapting to dynamic sailing environments. Its efficiency and effectiveness are demonstrated by six case study voyages from a chemical tanker with full-scale measurements, and further compared with other widely used voyage optimization methods. The results show that the proposed method can provide smooth routes with subtle turns with 5% fuel reduction on average for all case voyages, with around 40 seconds runtime.