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Maximum Observation of a Target by a Slower Observer in Three Dimensions

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第3期44卷 646-653页

作者： Weintraub, Isaac E. Von Moll, Alexander Garcia, Eloy Pachter, Meir Air Force Res Lab Aerosp Vehicles Technol Assessment & Simulations Wright Patterson AFB OH 45433 USA Air Force Res Lab Control Sci Ctr Excellence Wright Patterson AFB OH 45433 USA Air Force Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

Researchers consider the observation of a faster, non-maneuvering target by a slower observer in 3-dimensional (3-D) Cartesian space, rather than in the plane. The observation problem is commonly referred to as intelligence, surveillance, and reconnaissance (ISR). Observation of a slower ground vehicle by a single or team of faster aerial platforms has been considered. Researchers have also considered the tracking problem of a ground moving target in an urban terrain, with and without airspace limitations. They have considered the task of tracking the ground vehicle by either a single or team of unmanned air vehicles. To handle the computational complexity from numerous mobile agents, the researchers made use of an evolutionary algorithm (EA) to find optimal strategies for the multiple UAVs.

关键词： Flight Path Angle Transversality Condition Heading Angle Unmanned Aerial Vehicle nonlinear Dynamics nonlinear programming Evolutionary Algorithm Air Vehicle Quadrotor Computing

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Optimal Real-Time Approach and Capture of Uncontrolled Spacecraft

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JOURNAL OF SPACECRAFT AND ROCKETS 2021年第6期58卷 1762-1773页

作者： Li, Hongjue Dong, Yunfeng Li, Peiyun Deng, Yue Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simul Beijing 100191 Peoples R China Sci & Technol Space Intelligent Control Lab Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China

An optimal real-time neural-network-based controller for an on-orbit service mission is proposed. The problem can be mathematically formulated as a complex optimal control problem due to the coupling nature of the orbit, the attitude, and the manipulator of the chaser spacecraft. A hierarchical optimization procedure is developed to efficiently solve the problem by recursively applying three optimization modules. The relative motion characteristics and the decoupled wrist-arm feature are used to transform the solved optimal solutions into easier learnable samples. A series of deep neural network (DNN) controllers are designed to learn from these samples. The performances of the trained network controllers are analyzed by altering the number of learned trajectories and the structure of networks. Simulation results illustrate that the designed DNN controllers can successfully guide a chaser to approach and capture an uncontrolled target with tolerable errors.

关键词： Spacecrafts Manipulators Trajectory Optimization Convolutional Neural Network Inverse Kinematics Real Time Optimization Euler Angles Attitude Dynamics Proportional Integral Derivative nonlinear programming

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On the Weak Stationarity Conditions for Mathematical Programs with Cardinality Constraints: A Unified Approach

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APPLIED MATHEMATICS AND OPTIMIZATION 2021年第3期84卷 3451-3473页

作者： Krulikovski, Evelin H. M. Ribeiro, Ademir A. Sachine, Mael Univ Fed Parana Curitiba Parana Brazil

In this paper, we study a class of optimization problems, called Mathematical Programs with Cardinality Constraints (MPCaC). This kind of problem is generally difficult to deal with, because it involves a constraint that is not continuous neither convex, but provides sparse solutions. Thereby we reformulate MPCaC in a suitable way, by modeling it as mixed-integer problem and then addressing its continuous counterpart, which will be referred to as relaxed problem. We investigate the relaxed problem by analyzing the classical constraints in two cases: linear and nonlinear. In the linear case, we propose a general approach and present a discussion of the Guignard and Abadie constraint qualifications, proving in this case that every minimizer of the relaxed problem satisfies the Karush-Kuhn-Tucker (KKT) conditions. On the other hand, in the nonlinear case, we show that some standard constraint qualifications may be violated. Therefore, we cannot assert about KKT points. Motivated to find a minimizer for the MPCaC problem, we define new and weaker stationarity conditions, by proposing a unified approach.

关键词： Mathematical programs with cardinality constraints nonlinear programming Sparse solutions Constraint qualification Weak stationarity

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An Optimization Model for the Optimal Control of the Oxygen Excess Ratio in a Hydrogen Fuel Cell System

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IFAC-PapersOnLine 2024年第13期58卷 496-501页

作者： Yassine Ennassiri Giulio Ferro Loredana Magistri Michela Robba DIBRIS—Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa 16145 Italy Thermochemical Power Group (TPG) University of Genoa Via Montallegro 1 16145 Genoa Italy

This paper focuses on the control of a fuel cell system, for which an optimization model is proposed to control the oxygen excess ratio in order to tackle the fuel cell starvation phenomenon and increase the net power production of the system. The optimization problem considers the detailed nonlinear dynamics of the stack and the air supply sub-system as constraints and upper and lower bounds to consider the technical limitations in the system’s operation. The goal is to maintain the oxygen excess ratio at its reference value, using the compressor input voltage as a control variable, under several load variations of the current demand, considered as the external noise to the system. The proposed nonlinear optimization problem has been applied to a 75 kW stack used in the Ford P2000 fuel cell prototype vehicles, and results have been compared to the proportional-integral (PI) control technique. The proposed control approach has shown a significant enhancement in the control performance of the oxygen excess ratio.

关键词： fuel cell nonlinear programming optimization oxygen excess ratio

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An Analytical Approach to Machine Layout Design at a High-Pressure Die Casting Manufacturer

An Analytical Approach to Machine Layout Design at a High-Pr...

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21st International Symposium on Production Research (ISPR) - Digitizing Production System

作者： Emir, Oguz Aktin, Tulin Istanbul Kultur Univ Dept Ind Engn Istanbul Turkey

ISBN: (纸本)9783030904210;9783030904203

Sahin Metal was founded in 1975 on a 7500 m(2) area *** to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various routings through 14 workstations in the plant. Being a Tier 2 company, these products are then sent to Tier 1 firms and finally reach the leading vehicle manufacturers. Nowadays, increasing competition, changing customer demands, and quality targets bring the necessity of restructuring internal processes. In this regard, Sahin Metal plans to rearrange the existing machine layout to minimize the distance traveled between departments by taking into account the material flow. This study aims to determine an efficient machine layout design by implementing analytical approaches. The study is started by visiting the production facility and meeting with the company's engineers to determine the project roadmap. Following that, the data collection process is initiated, and ABC analysis is performed to define product classes. After this identification, two approaches are utilized simultaneously. The Hollier method is employed to find a logical machine arrangement. In addition, a mathematical model based on the Quadratic Assignment Problem (QAP) is developed to obtain the optimum machine layout. The developed integer nonlinear model is solved by CONOPT using GAMS software under various scenarios. Finally, these results are compared with the existing system, and a convenient layout design is proposed to the company.

关键词： Facilities design and planning Machine layout design nonlinear programming Quadratic assignment problem Hollier method

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An alternating direction method of multipliers for the eigenvalue complementarity problem

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OPTIMIZATION METHODS & SOFTWARE 2021年第2-3期36卷 337-370页

作者： Judice, Joaquim J. Fukushima, Masao Iusem, Alfredo Martinez, J. M. Sessa, Valentina Inst Telecomunicacoes Coimbra Portugal Nanzan Univ Fac Sci & Technol Nagoya Aichi Japan IMPA Rio De Janeiro Brazil Univ Estadual Campinas Dept Appl Math IMECC UNICAMP Campinas Brazil PSL Res Univ MINES ParisTech CMA Sophia Antipolis France

We introduce an Alternating Direction Method of Multipliers (ADMM) for finding a solution of the nonsymmetric Eigenvalue Complementarity Problem (EiCP). A simpler version of this method is proposed for the symmetric EiCP, that is, for the computation of a Stationary Point (SP) of a Standard Fractional Quadratic Program. The algorithm is also extended for the computation of an SP of a Standard Quadratic Program (StQP). Convergence analyses of these three versions of ADMM are presented. The main computational effort of ADMM is the solution of a Strictly Convex StQP, which can be efficiently solved by a Block Principal Pivoting algorithm. Furthermore, this algorithm provides a stopping criterion for ADMM that improves very much its efficacy to compute an accurate solution of the EiCP. Numerical results indicate that ADMM is in general very efficient for solving symmetric EiCPs in terms of the number of iterations and computational effort, but is less efficient for the solution of nonsymmetric EiCPs. However, ADMM is able to provide a good initial point for a fast second-order method, such as the so-called Semismooth Newton method. The resulting hybrid ADMM and SN algorithm seems to be quite efficient in practice for the solution of nonsymmetric EiCPs.

关键词： Complementarity problems eigenvalue problems nonlinear programming global optimization

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Algorithms for Interpretable High-Dimensional Regression

Algorithms for Interpretable High-Dimensional Regression

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作者： Sarwar, Owais Carnegie Mellon University

学位级别：Ph.D., Doctor of Philosophy

Sparse linear regression is a vast field and there are many different algorithms available to build models. We start here by performing a meta-analysis of the literature to provide recommendations to users about when to use which regression algorithm. We then consider Best Subset Selection (BSS), which constructs sparse linear regression models by selecting a subset of potential regression variables to include into the regression model to minimize prediction error while also minimizing complexity. The combinatorial nature of the problem means that BSS may be impractical to solve in many instances. We propose new approximate methods that round the QP-relaxation of the MIQP formulation of the BSS problem to construct good approximate solutions for large BSS problems. We provide theoretical analysis of our algorithms for special cases to give insight into why they perform well. Empirically, our rounding algorithms outperform other popular approaches for subset selection. Building on our work on subset selection, we propose the flexible Regularized Subsets framework that performs variable selection and coefficient shrinkage in two-stages: first using any subset selection method to select variables, then using a convex penalty to estimate coefficients. This approach encourages both sparsity and robustness. We show that Regularized Subsets with rounding for variable selection outperforms other linear model-building approaches in a variety of problem settings. In addition, Regularized Subsets is able to build models from a candidate set of thousands of variables in seconds. Finally, we consider the constrained regression problem. Modelers would often like to further improve accuracy by imposing constraints on the regression coefficients to incorporate a priori knowledge into the linear regression algorithm. We propose the flexible Linearly-Constrained Regularized Subsets framework for this task. The Linearly-Constrained Regularized Subsets framework can easily handle dozen

关键词： Sparse linear regression Machine learning nonlinear programming

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Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第5期22卷 2969-2978页

作者： Xu, Zhigang Wang, Yu Wang, Guanqun Li, Xiaopeng Bertini, Robert L. Qu, Xiaobo Zhao, Xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA Chalmers Univ Technol Dept Architecture & Civil Engn S-41296 Gothenburg Sweden

Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to similar to 450 s for ETO.

关键词： Connected automated vehicle fuel economy speed control trajectory optimization shooting heuristics nonlinear programming

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Onboard Generation of Optimal Trajectories for Hypersonic Vehicles Using Deep Learning

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JOURNAL OF SPACECRAFT AND ROCKETS 2021年第2期58卷 400-414页

作者： Shi, Yang Wang, Zhenbo Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA

Recent development of deep learning has shown that a deep neural network (DNN) is capable of learning the underlying nonlinear relationship between the state and the optimal actions for nonlinear optimal control problems. In terms of hypersonic flight, this suggests that the DNN-based trajectory controller may be considered to take over all or part of the onboard trajectory generation and guidance system. This Paper investigates the possibility of training the DNN-based controller off line using the optimal state-action samples obtained from high-fidelity algorithms. The time-consuming computation is carried out off line, and the resulting DNN-based controller is potentially capable of near-optimal control with real-time performance and stable convergence. First, the system dynamics of hypersonic flight are described, and the trajectory optimization problem is formulated as a highly nonlinear optimal control problem. Then, the state-action vectors are extracted from the optimal trajectories generated by solving the formulated optimal control problem from random initial states using a homotopy method. Thereafter, DNNs are designed to learn the functional relationship between the flight states and the optimal actions to enable the capability of optimal action predictions. At last, the performance of the proposed approach is demonstrated through numerical simulations.

关键词： Feedforward Neural Network Hypersonic Vehicles Hamilton Jacobi Bellman Equation Hypersonic Flight Guidance System Numerical Simulation Flight Path Angle Lift Coefficient Earth nonlinear programming

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Convex Approach to Three-Dimensional Launch Vehicle Ascent Trajectory Optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第6期44卷 1116-1131页

作者： Benedikter, Boris Zavoli, Alessandro Colasurdo, Guido Pizzurro, Simone Cavallini, Enrico Sapienza Univ Rome Dept Mech & Aerosp Engn Via Eudossiana 18 I-00184 Rome Italy Italian Space Agcy Launchers & Space Transportat Dept Via Politecn Snc I-00133 Rome Italy Italian Space Agcy Sci Res Dept Solid Prop & Launch Vehicle Syst Eng Via Politecn Snc I-00133 Rome Italy

This paper deals with the optimization of the ascent trajectory of a multistage launch vehicle, from liftoff to the payload injection into the target orbit, considering inverse-square gravity acceleration and aerodynamic forces. A combination of lossless and successive convexification techniques is adopted to generate a sequence of convex problems that rapidly converges to the original problem solution. An automatic initialization strategy is proposed to make the solution process completely autonomous. In particular, a novel three-step continuation procedure is developed and proved to be more efficient than simpler strategies. This approach relies on the solution of intermediate problems, which either neglect atmospheric drag or fix the time-lengths of the launch vehicle ascent phases, that are solved in succession, gradually passing from easier instances of the optimization problem to the originally intended problem. State-of-the-art techniques to deal with such a complex problem are adopted to enhance the convergence rate, including safeguarding modifications, such as virtual controls and an adaptive trust region. To assess the validity of the proposed approach in a practical scenario, numerical results are presented for two representative practical applications, using as reference a Falcon 9 launch vehicle.

关键词： Launch Vehicle Configurations Trajectory Optimization Aerodynamic Force Atmospheric Drag Propellant Rocket Trajectories Earth nonlinear programming Three Degrees of Freedom Adaptive Mesh Refinement

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