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American Control Conference

作者： Masih Haseli Jorge Cortes University of California San Diego CA 92093 USA

ISBN: (纸本)9781538679012;9781538679265

This paper presents a data-driven method to find a finite-dimensional approximation for the Koopman operator using noisy data. The proposed method is a modification of Extended Dynamic Mode Decomposition which finds an approximation for the projection of the Koopman operator on a subspace spanned by a predefined dictionary of functions. Unlike the Extended Dynamic Mode Decomposition which is based on least squares method, the presented method is based on element-wise weighted total least squares which enables one to find a consistent approximation when the data come from a static linear relationship and the noise at different times are not identically distributed. Even though the aforementioned method is consistent, it leads to a nonconvex optimization problem. To alleviate this problem, we show that under some conditions the nonconvex optimization problem has a common minimizer with a different method based on total least squares for which one can find the solution in closed form.

关键词： concave programming data analysis least squares approximations

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Tertiary Control of Islanded Microgrids Based on a Linearized ACOPF with Losses Compensation

Tertiary Control of Islanded Microgrids Based on a Linearize...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Jun Long Cheng Gong Yidan Lu College of Electrical Engineering GuangXi University NanNing China

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

A tertiary control based on linearized AC optimal power flow(OPF) for islanded microgrids is developed. Nonlinear power flow constraints typically make AC OPF problems nonconvex and computationally intractable. To address this concern, a lossless linearized power flow (LPF) for islanded microgrids is proposed that does not assume a fixed slack bus voltage to convexify AC OPFs under some reasonable assumptions. The approximation error of LPF is examined via norm inequality in finite-dimensional vector space. The geometry of the proposed LPF model is then explored and the effect of fixed voltage assumptions on power generation is studied. As OPFs based on lossless power flow cannot limit power losses when minimizing operational cost as an AC OPF does, a linearized AC OPF model that minimizes operational cost and active power losses as the core of the tertiary control is developed. Moreover, a losses compensation algorithm is proposed to supply power losses using dispatchable generators. Case studies demonstrate that the proposed approach provides substantial computational speed improvements with minimal errors compared to a typical AC OPF.

关键词： approximation theory compensation concave programming cost reduction distributed power generation geometry load flow control power generation control power generation dispatch

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Energy Harvesting Transmitter with Finite-Alphabet Inputs and Statistical CSI: Online Precoding by Dynamic programming

Energy Harvesting Transmitter with Finite-Alphabet Inputs an...

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IEEE Global Communications Conference

作者： Weiliang Zeng Yahong Rosa Zheng Chengshan Xiao Department of Electrical & Computer Engineering Missouri University of Science and Technology

ISBN: (纸本)9781479935130

This paper considers the online multi-antenna pre-coder design for energy harvesting transmitter with emphasis on finite-alphabet inputs and statistical channel state information (CSI). It formulates the problem by maximizing the sum of average mutual information of multiple time slots with causal energy constraint. This formulation leads to a 2N_t~2-dimensional stochastic dynamic programming (SDP) problem with a non-concave objective function, where N_t is the number of transmit antennas. The 2N_t~2-dimensional SDP problem is prohibitively difficult to solve and prevents the development of efficient online solver. We provide an equivalence between the multi-dimensional SDP problem and a one-dimensional power choice problem. Solving the one-dimensional equivalence decreases the computational burden extraordinarily without loss of optimality. We also demonstrate the performance gains offered by the proposed method when compared with other algorithms.

关键词： antenna arrays concave programming dynamic programming energy harvesting precoding radio transmitters statistical analysis stochastic programming telecommunication power supplies transmitting antennas 2 N2t-dimensional stochastic dynamic programming problem average mutual information causal energy constraint energy harvesting transmitter finite-alphabet inputs multidimensional SDP problem multiple time slots nonconcave objective function one-dimensional power choice problem online multiantenna precoder design online precoding statistical CSI statistical channel state information transmit antennas Batteries Complexity theory Energy harvesting MIMO Phase shift keying Signal to noise ratio Transmitters

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Model Predictive Control of a Wave Energy Converter Using Duality Techniques

Model Predictive Control of a Wave Energy Converter Using Du...

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American Control Conference

作者： Alyssa Kody Nathan Tom Jeffrey Scruggs University of Michigan Ann Arbor MI 48105 USA National Renewable Energy Laboratory 15013 Denver West Parkway Golden CO 80401 USA

ISBN: (纸本)9781538679012;9781538679265

In this paper, a method for implementing model predictive control for vibration energy harvesting systems with the goal of maximizing generated energy is presented. This is generally a nonconvex problem, which can be computationally expensive and converging to a global minimum cannot be guaranteed. We introduce a dual-domain method to relax the nonconvex primal problem into a convex dual optimization problem, which can be performed in real time. Based on previous work, we determine when the duality gap is zero and, by extension, whether the global optimal primal solution is found. We demonstrate the algorithm on a single, buoy-type wave energy converter with energy storage constraints and power take-off force constraints.

关键词： concave programming duality (mathematics) energy harvesting minimisation power system control predictive control quadratic programming vibrations wave power generation

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Energy-Efficient Resource Allocation for Latency-Sensitive Mobile Edge Computing

Energy-Efficient Resource Allocation for Latency-Sensitive M...

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IEEE Vehicular Technology Conference

作者： Xihan Chen Yunlong Cai Qingjiang Shi Minjian Zhao Guanding Yu Zhejiang University College of ISEE Hangzhou China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing 210016 China

ISBN: (纸本)9781538663592;9781538663585

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

关键词： computational complexity concave programming convex programming energy conservation mobile computing multi-access systems radiofrequency interference resource allocation telecommunication power management wireless channels Resource utilization Multi access systems Radiofrequency interference wireless channels convex programming Mobile Computing complexity classes Energy conservation UNLOADING management of resources system energy consumption Mobile Interference channels power level

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Two-layer optimization to cooperative conflict detection and resolution for UAVs

Two-layer optimization to cooperative conflict detection and...

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International IEEE Conference on Intelligent Transportation Systems

作者： Jian Yang Dong Yin Qiao Cheng Xu xie Dept. of Mechatron. & Autom. Nat. Univ. of Defense Technol. Changsha China

ISBN: (纸本)9781467365970

This paper focuses on the solution for conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs) by heading control. The cooperative method is proposed. First, the relationships between conflicts involved UAVs are described by the geometric method. The practical and potential conflicts are considered. Then, the CDR problem is formalized as a nonlinear optimization problem so as to minimize maneuver costs. Moreover, a two-layer strategy composed of stochastic parallel gradient descent (SPGD) and interior-point algorithm is designed to efficiently solve the non-convex optimization problem. Finally, our approach is demonstrated on several scenarios and the simulation results show that it can achieve high performance in obtaining near optimal maneuvers for UAVs CDR.

关键词： autonomous aerial vehicles concave programming cooperative systems geometry gradient methods mobile robots nonlinear programming stochastic processes CDR SPGD UAV conflict detection and resolution cooperative method geometric method interior-point algorithm nonconvex optimization nonlinear optimization stochastic parallel gradient descent unmanned aerial vehicle Airplanes Algorithm design and analysis Fuels Optimization Safety Trajectory Vehicles airspace integration conflict resolution optimization unmanned aerial vehicles Unmanned Aerial Vehicle drone honey bees vehicle Interior point algorithm Algorithm design and analysis plane cooperative systems gradient methods RECORDABLE COMPACT DISC CONCEPTUAL DESIGN REPORT RUNX1T1 gene AKR1C4 gene Conflict Resolution Nonlinear programming conflict detection

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Optimal Power Allocation and Signal Phase Selection in NOMA Wireless Relay Networks

Optimal Power Allocation and Signal Phase Selection in NOMA ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Yen-Chun Liu Rung-Hung Gau Institute of Communications Engineering National Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9781728138930

ISBN: (纸本)9781728138947

In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.

关键词： concave programming greedy algorithms relay networks (telecommunication) signal detection telecommunication power management power splitting greedy algorithm signal phase signal acquisition System performance power transmission Optimal

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A Note on the Maximum Points of the Minimum of Quasiconvexs Functions

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Optimization 1985年第5期16卷 669-671页

作者： Mathar, R. Technische Hochschule Aachen Institut für Statistik und Wirtschaftsmathematik Aachen D - 5100 Wüllnerstr. 3 Germany

The paper deals with the problem of maximizing the minimum of quasiconvex functions over a compact convex set Z. Subsets of Z are given which contain all solutions or at least one solution. © 1985, Taylor & F... 详细信息

关键词： concave programming Quasiconvex functions

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On Convergence Of Convex Minorant Algorithms For Distribution Estimation With Interval-Censored Data

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Journal of Computational and Graphical Statistics 1992年第2期1卷 129-140页

作者： Aragon, Jorge Eberly, David Harvard School of Public Health Biostatistics Department Boston MA 02115 United States University of Texas at San Antonio Division of Mathematics Computer Sciences and Statistics San Antonio TX 78249 United States

Local convergence results of the convex minorant (CM) algorithm to obtain the nonparametric maximum-likelihood estimator of a distribution under interval-censored observations are given. We also provide a variation of the CM algorithm, which yields global convergence. The algorithm is illustrated with data on AIDS survival time in 92 members of the U. S. Air Force. © 1992 Taylor & Francis Group, LLC.

关键词： Censored data concave programming Convex minorant Newton-Jacobi method Nonparametric maximum likelihood

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Low probability of intercept-based collaborative transmit resource and bandwidth allocation strategy for target tracking in distributed radar network system

Low probability of intercept-based collaborative transmit re...

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IET International Radar Conference (IET IRC 2020)

作者： L. Ding C. Shi F. Wang J. Zhou Nanjing Univ. Aeronaut. Astronaut. Nanjing China

In this study, a collaborative transmit resource and bandwidth allocation (CTRBA) strategy based on the low probability of intercept (LPI) is presented for target tracking in the distributed radar network system. The primary goal of this strategy is to minimize the total resource consumption while satisfying a predetermined target tracking performance requirement. The closed-form expression for the predicted Bayesian Cramer-Rao lower bound (BCRLB) is derived and computed to gauge the target tracking accuracy. It is shown that the CTRBA is a mixed-Boolean and non-convex optimization problem. We propose an efficient two-step solution methodology to solve the resulting problem. Simulation results demonstrate that the CTRBA strategy can achieve the desired tracking performance with less resource consumption.

关键词： optimisation bandwidth allocation concave programming target tracking convex programming Bayes methods radar tracking probability

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