1,721,224 research outputs found
A distributed, rolling-horizon demand side management algorithm under wind power uncertainty
No full textIn this paper, we consider a smart grid where users behave selfishly, aiming at minimizing cost in the presence of uncertain wind power availability. We adopt a demand side management (DSM) model, where active users (so-called prosumers) have both private generation and local storage availability. These prosumers participate to the DSM strategy by updating their energy schedule, seeking to minimize their local cost, given their local preferences and the global grid constraints. The energy price is defined as a function of the aggregate load and the wind power availability. We model the resulting problem as a non-cooperative Nash game and propose a semi-decentralized algorithm to compute an equilibrium. To cope with the uncertainty in the wind power, we adopt a rolling-horizon approach, and in addition we use a stochastic optimization technique. We generate several wind power production scenarios from a defined probability density function (PDF), determining an approximate stochastic cost function. Simulations results on a real dataset show that the proposed approach generates lower individual costs compared to a standard expected value approach.Team Bart De SchutterTeam Sergio Grammatic
Reducing the computational effort of MPC with closed-loop optimal sequences of affine laws
Get PDFWe consider the classical infinite-horizon constrained linear-quadratic regulator (CLQR) problem and its receding-horizon variant used in model predictive control (MPC). If the terminal constraints are inactive for the current initial condition, the optimal input signal sequence that results for the open-loop CLQR problem is equal to the closed-loop optimal sequence that results for MPC. Consequently, the closed-loop optimal solution is available from solving only one CLQR problem instead of the usual infinite number of CLQR problems solved on the receding horizon. In the presence of disturbances or because of plant-model mismatch, the system will eventually leave the predicted optimal trajectory. Consequently, the solution of the single open-loop CLQR problem is no longer optimal, and the receding horizon problem must resume. We show, however, that the open-loop solution is also robust. Robustness essentially is given, because the solution of the CLQR problem not only provides the sequence of nominally optimal input signals, but a sequence of optimal affine laws along with their polytopes of validity. We analyze the degree of robustness by computational experiments. The results indicate the degree of robustness is practically relevant
Smart and efficient: Learning curves in manual and human-robot order picking systems
Get PDFOrder picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semiautomated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems
Drugs Cross-Distribution Management in Urban Areas through an Incentives Scheme
No full textThe modern societies have witnessed several developments and changes in cities in the recent years. In order to make cities smart, new technological infrastructures are required to connect networks of actors, sensors and actuators embedded throughout the urban ground, and to interact with wireless mobile devices. In this context, this paper proposes an innovative approach for optimizing drug delivery and cost saving, inspired by the collaborative urban logistics concept. More in detail, the proposed approach is based on the use of a shared city warehouse managed by a network of pharmacies where it is possible to pick up the missing drugs. The paper develops a pharmacy supplying method based on an incentive system to engage pharmacists in the drug distribution process. An interactive drug distribution algorithm, based on an Integer Linear Programming problem, is proposed to minimize the external and transport costs. Finally, a case study is introduced, and the method efficiency is shown through the related simulations
A self-triggered control scheme for Markov jump systems under multiple range performance restrictions
No full textThis paper proposes a multi-frequency controller design scheme for Markov jump systems (MJSs) based on the self-triggered strategy in a resource-aware way. Firstly, a derandomization technique is introduced to make sure the transition probability information is included in the finite frequency specification analysis. Then, a self-triggered policy is developed to update the control input of the system via the history measurement. Finally, sufficient conditions are deduced that guarantee the multiple range frequency performances and the reduction of computation and communication occupation for the controlled MJSs, simultanously. The cart- spring system is employed to illustrate the effectiveness of the proposed approach
A multi-commodity flow problem for fair resource allocation in multi-path video delivery networks
Get PDFVideo streaming services employ the Internet to distribute content to an ever-increasing number of concurrent viewers. The delivery architecture employed by leading video platforms requires players to run a control algorithm dynamically choosing the video bitrate to match the time-varying network bandwidth and avoid playback interruptions due to buffer underruns. Such an algorithm is generally designed to (selfishly) improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users and not to a distribution of resources that is fair in terms of user perceived quality. We argue that video service providers should manage their delivery network to address fairness issues to gracefully degrade the perceived quality equally for all users when resources become scarce. Even though the general problem of providing a fair level of perceived quality does not scale with the cumbersome number of concurrent users, this paper shows that the Multi-Commodity Flow Problem (MCFP) optimization framework is a proper and efficient tool to address this open issue. First, we show how to cast the resource allocation problem to an MCFP and then we propose a strategy to make the resulting problem tractable for video distribution platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is assessed using realistic simulations involving thousands of concurrent video sessions on a real network topology by varying both the total load on the network and key system parameters
Decentralized Optimal Coverage Control for Constant-Speed Unicycle Multi-Agent Systems
No full textThis dataset contains the program code for the following publication: Liu, Qingchen, Zengjie Zhang, Nhan Khanh Le, Jiahu Qin, Fangzhou Liu, and Sandra Hirche. "Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems." IEEE Transactions on Automation Science and Engineering (2024). It can be used for coverage control of fixed-wing drones
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Experimental evaluation of outliers filtering techniques in networked acoustic localisation systems
Get PDFLocalisation-aware underwater networks are gaining increasing attention in the marine robotics community thanks to their ability of providing navigational services. This can be beneficial in a number of applications, as for instance to support the navigation of Autonomous Underwater Vehicles (AUVs) when traditional aiding systems are impractical or not cost effective. However, the unreliability of the acoustic channel, together with the additional overhead and constraints introduced by the network itself, result in localisation measurements that are intrinsically sporadic and unpredictable. This makes the outlier filtering problem of localisation measurements obtained through networked underwater systems particularly important and challenging. The presence of one or more wrong observations can heavily impact on the performance of the localisation system and hence resulting in poor navigation. This paper uses experimental data to compare the integration of two different outlier filtering methodologies in an existing network-aided AUV navigation filter. The first method aims at pre-filtering the measurements to identify and discard potential outliers before they are fused in the navigation filter. The second one modifies the correction step of the Kalman filter to integrate measurements in an outlier-robust way. Results shows that when the navigation filter is made outlier-robust the navigation performance increases and the system becomes less sensitive to tuning, a key characters tic for fielded systems
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