Searched for: subject%3A%22Prediction%255C+algorithms%22
(1 - 10 of 10)
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Zattoni Scroccaro, P. (author), Sharifi K., Arman (author), Mohajerin Esfahani, P. (author)
In the past few years, online convex optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this article, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics,...
journal article 2023
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Du, Guodong (author), Zou, Yuan (author), Zhang, Xudong (author), Li, Z. (author), Liu, Qi (author)
The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking...
journal article 2023
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Zou, L. (author), Zhan, Xiu xiu (author), Sun, Jie (author), Hanjalic, A. (author), Wang, H. (author)
Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods...
journal article 2022
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Zhang, Xinglong (author), Peng, Yaoqian (author), Pan, W. (author), Xu, Xin (author), Xie, Haibin (author)
Distributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL)...
conference paper 2022
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Dutta, Shamak (author), Wilde, N. (author), Smith, Stephen L. (author)
We present a new mixed integer formulation for the discrete informative path planning problem in random fields. The objective is to compute a budget constrained path while collecting measurements whose linear estimate results in minimum error over a finite set of prediction locations. The problem is known to be NP-hard. However, we strive to...
conference paper 2022
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van Wingerden, J.W. (author), Mulders, S.P. (author), Dinkla, R.T.O. (author), Oomen, T.A.E. (author), Verhaegen, M.H.G. (author)
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence...
conference paper 2022
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Cavone, Graziana (author), van den Boom, A.J.J. (author), Blenkers, Lex (author), Dotoli, Mariagrazia (author), Seatzu, Carla (author), De Schutter, B.H.K. (author)
Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling...
journal article 2022
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Muhammad Iqbal, M.A.F. (author), Kuipers, F.A. (author)
A cyber-physical system is often designed as a network in which critical information is transmitted. However, network links may fail, possibly as the result of a disaster. Disasters tend to display spatiotemporal characteristics, and consequently link availabilities may vary in time. Yet, the requested connection availability of traffic must be...
conference paper 2016
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Munk, J. (author), Kober, J. (author), Babuska, R. (author)
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it...
conference paper 2016
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Xu, J. (author), van den Boom, A.J.J. (author), Busoniu, L (author), De Schutter, B.H.K. (author)
This paper considers model predictive control for continuous piecewise affine (PWA) systems. In general, this leads to a nonlinear, nonconvex optimization problem. We introduce an approach based on optimistic optimization to solve the resulting optimization problem. Optimistic optimization is based on recursive partitioning of the feasible set...
conference paper 2016
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