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Finite-Thrust Natural-Motion Circumnavigation Injection by Convex Optimization

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第3期45卷 453-467页

作者： Lu, Ping Lewis, Alexander Adams, Richard J. DeVore, Michael D. Petersen, Christopher D. San Diego State Univ San Diego CA 92182 USA Barron Associates Inc Charlottesville VA 22901 USA US Air Force Res Lab US Airtland Air Force Base Albuquerque NM 87117 USA

A methodology is developed to use convex optimization for finding the propellant-optimal finite-thrust trajectory of a spacecraft to inject into a specified natural-motion circumnavigation (NMC) orbit around another spacecraft. The problem is nonconvex. A philosophically new perspective is introduced to take advantage of modern convex optimization. Through a novel analysis the NMC problem is shown to be equivalent to a two-dimensional constrained optimization problem. This conceptually simpler interpretation enables two numerical approaches to be investigated, one based on convex relaxation and the other linearization-projection. It is established that while each approach is able to lead to the solution to the NMC-injection problem in a subset of the possible cases, their domains of applicability complement each other to cover all possible cases. A hybrid algorithm is designed that combines the strengths of the two approaches and enables the application of convex optimization to solve the NMC-injection problem in all cases where the solution exists, without the need for any user-supplied parameters or initial guesses. The effectiveness of the hybrid algorithm is demonstrated in finding the numerical solutions to the NMC problem reliably and rapidly.

关键词： Thrust Nonlinear programming Propellant Thrust Vectoring sequential quadratic programming Orbital Elements Optimal Control Model Predictive Control Earth Spacecraft Formation

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Fast Quantum Gate Control with Trajectory Optimization

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IFAC-PapersOnLine 2024年第14期58卷 331-336页

作者： Shouliang Hu Ming Li Chunlin Chen Daoyi Dong School of Engineering Australian National University Canberra Australia School of Automation Guangdong University of Technology Guangdong China School of Management and Engineering Nanjing University Nanjing China CIICADA Lab School of Engineering Australian National University Canberra Australia

Fast quantum control helps reduce the influence of unavoided disturbances and hence plays a vital role in practical quantum technology and chemical reactions. Instead of optimizing the terminal cost like standard optimal quantum control methods, this paper formulates the problem as a trajectory optimization problem, and implements the sequential quadratic programming algorithm to search for short control fields. The core idea is to minimize the cumulative intermediate error to incentivize early achievement of the designed gate. The numerical result on the Toffoli gate demonstrates the effectiveness of the proposed method.

关键词： quantum gate trajectory optimization sequential quadratic programming

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Neural Network-Based Trajectory Optimization for Unmanned Aerial Vehicles

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2012年第2期35卷 548-562页

作者： Horn, Joseph F. Schmidt, Eric M. Geiger, Brian R. DeAngelo, Mark P. Penn State Univ University Pk PA 16802 USA Piasecki Aircraft Corp Unmanned Aerial Vehicle Projects & Controls Essington PA 19029 USA

A direct trajectory optimization method that uses neural network approximation methods is presented. Neural networks are trained to approximate objective functions and vehicle dynamics. The neural network method reduces computational requirements by removing the need for collocation and providing fast computation of gradients when compared with direct and pseudospectral collocation methods. The method is shown to significantly reduce computational costs while resulting in trajectories comparable to those produced by direct collocation and pseudospectral methods. Because a neural network readily provides accurate computation of gradients, it removes the need for formulating analytical gradients;thus, the method is more easily extended to different types of applications with different objective functions and constraints. This paper demonstrates the flexibility of the neural network trajectory optimization approach through simulation of three cases: a single unmanned aerial vehicle operating a fixed camera, multiple unmanned aerial vehicles operating fixed cameras, and a single unmanned aerial vehicle operating a gimballed camera. The method's ability to produce tightly constrained trajectories is also demonstrated.

关键词： Trajectory Optimization Feedforward Neural Network UAV Chebyshev Pseudospectral Method Collocation Method Nonlinear programming MATLAB Bank Angle True Airspeed sequential quadratic programming

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Fault Location Observability Using Phasor Measurement Units in a Power Network Through Deterministic and Stochastic Algorithms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第3期47卷 212-229页

作者： Theodorakatos, Nikolaos P. Natl Tech Univ Athens ECE Dept Iroon Polytech 9 Athens 15780 Greece

This article studies deterministic and stochastic algorithms for placing minimum number of phasor measurement units (PMUs) in a power system in order to locate any fault in the power system. The optimization problem is initially formulated in a mixed integer linear programing framework with binary-valued variables as well as in a binary integer linear programing model. Then, the optimization problem is formulated as an equivalent non-linear programing model, minimizing a quadratic objective function subject to equality non-linear constraints defined over a bounded and closed set. The problem is solved by using a sequential quadratic programming algorithm. The non-linear program is illustrated with a 7-bus test system. Also, stochastic algorithms such as binary-coded genetic algorithm and particle swarm optimization have been implemented in solving the optimal PMU placement under fault condition. The accuracy of suggested algorithms is independent from the fault type and its resistance. The optimization models are applied to the IEEE systems. The numerical results indicate that the proposed algorithms locate minimizers at the optimal objective function value in complete agreement with those obtained by branch-and-bound algorithms.

关键词： phasor measurement unit fault network observability mixed integer programming branch-and-bound algorithm non-linear programming sequential quadratic programming genetic algorithm particle swarm optimization

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Optimization of geometrically nonlinear thin shells subject to displacement and stability constraints

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AIAA JOURNAL 2007年第3期45卷 684-692页

作者： Khosravi, Peyman Sedaghati, Ramin Ganesan, Rajamohan Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada

A methodology is developed for shape optimization of thin plate and shell structures undergoing large deflections subject to displacement and system stability constraints. The optimization method considers shape parameters and overall thickness of the structure as the design variables and aims to minimize the total mass of the structure subject to stability or displacement constraint. Two optimality criteria based on Karush-Kuhn-Tucker conditions are developed for mass minimization problems. Optimality criteria are combined with nonlinear corotational analysis to optimize structures with geometric nonlinearity. The method is applied to plate and shallow shell structures. The efficiency of the developed design optimization methodology is compared with that of the gradient-based method of optimization (sequential quadratic programming).

关键词： sequential quadratic programming Finite Element Solution Energy Density Beam (Structures) High Aspect Ratio Shape Optimization Spherical Shell Triangular Element Poisson's Ratio Sensitivity Analysis

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Aerogravity-assist maneuvers: Coupled trajectory and vehicle shape optimization

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JOURNAL OF SPACECRAFT AND ROCKETS 2007年第5期44卷 1051-1059页

作者： Armellin, Roberto Lavagna, Michele Starkey, Ryan P. Lewis, Mark J. Politecn Milan Dept Aerosp Engn I-20156 Milan Italy Univ Maryland Dept Aerosp Engn College Pk MD 20742 USA

The aerogravity-assist maneuver is proposed as a tool to improve the efficiency of the gravity assist, because due to the interaction with the planetary atmosphere, the angular deviation of the velocity vector can be definitely increased. Even though the drag reduces the spacecraft velocity, the overall A v gain could be substantial for a high-lift-to-drag vehicle. A previous study addressed the three-dimensional dynamic modeling and optimization of the maneuver, including heliocentric plane change, heating rate, and structural load analysis. A multidisciplinary study of aerogravity assist is proposed, focusing on coupled trajectory and vehicle shape optimization. A planar aerogravity assist of Mars is selected as a test case, with the aim of maximizing the vehicle heliocentric velocity and limiting the heating rate experienced during the atmospheric pass. A multiobjective approach is adopted, and a particle swarm optimization algorithm is chosen to detect the set of Pareto-optimal solutions. The study includes a further refinement of the trajectory for three significant shapes belonging to the Pareto curve. The associated optimal control problem is solved by selecting a direct-method approach. The dynamics are transcribed into a set of nonlinear constraints, and the arising nonlinear programming problem is solved through a sequential quadratic programming solver.

关键词： Trajectory Optimization Aerogravity Assist Particle Swarm Optimization sequential quadratic programming Nonlinear programming Earth Atmosphere Structural Load Aerodynamic Coefficients Aerodynamic Performance Pressure Coefficient

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Integrating system-level and component-level designs under uncertainty

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JOURNAL OF SPACECRAFT AND ROCKETS 2005年第4期42卷 752-760页

作者： Smith, N Mahadevan, S Vanderbilt Univ Nashville TN 37235 USA

A probabilistic optimization methodology for aerospace vehicle design is developed that takes into account coupling between system-level and component-level design requirements. The proposed methodology represents the system-component design as a reliability-based multidisciplinary optimization, problem. The system design optimizes the geometry of a reusable launch vehicle for minimum weight while satisfying aerodynamic constraints. The component design relates to the structural sizing of vehicle components, in this case a liquid-hydrogen tank. The optimization formulation includes probabilistic constraints, which are evaluated using the limit state-based reliability analysis methodology. The system and component designs are linked through stochastic state variables relating to vehicle geometry and component weight, and the optimization at both levels is pursued. Finally, the reliability-based multidisciplinary optimization is solved using an efficient decoupled approach.

关键词： Reusable Launch Vehicle Aerodynamic Design Aerodynamic Performance Lift to Drag Ratio Probability Distribution Functions Control Surfaces Systems Engineering sequential quadratic programming Structural Failure Fuselages

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Interval analysis-based optimum design of wing structures under taxiing loads

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JOURNAL OF AIRCRAFT 2007年第3期44卷 1045-1049页

作者： Majumder, Luna Rao, S. S. Univ Miami Dept Mech & Aerosp Engn Coral Gables FL 33124 USA

The feasibility of performing the automated optimum design of airplane wing structures at preliminary design stages, with consideration of the taxing stress developed, is studied. During actual programming, we need to adjust the order in which different interval parameters are considered in any specific equation. The purpose of truncation is to make reasonable modifications to the output range before applying it to the next interval operation. This truncation approach can give reasonable predictions for the solution even when the widths of their starting points or the interval ranges of other influencing parameters are quite large. The optimization results obtained with the interval analysis are found to be in good agreement with those obtained with the deterministic approach. The interval analysis is expected to be more realistic and, hence, should be used in the optimum design of the airplane wing structure.

关键词： Aeroplane Landing Gear Angle of Attack Nonlinear programming Finite Element Method Aerodynamic Center Structural Weight sequential quadratic programming Internal Friction Aircraft Wings

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Dynamic production optimization for offshore Electric Submersible Pump oilfields based on oil-water two phase virtual flow metering

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PETROLEUM SCIENCE AND TECHNOLOGY 2024年

作者： Bai, Zonghan Kang, Qi Wu, Haihao Gong, Jing China Univ Petr Beijing Peoples R China Tsinghua Univ Tsinghua Univ Shenzhen Dept Automat Res Inst Shenzhen Peoples R China

Enhancing offshore oilfield production is critical, but optimizing production equipment parameters presents significant challenges due to the difficulty in obtaining accurate fluid properties and the necessity for precise equipment models. Multiphase flow calculations depend on preset fluid parameters, which are particularly challenging to determine in offshore environments. This paper proposes a virtual measurement method tailored for reservoir, well, and pipeline network models in offshore oilfields. The approach involves calibrating the Electric Submersible Pump (ESP) model and calculating the viscosity of oil-water emulsions to assess phase separation and fluid water content within the well. By utilizing revised data and production efficiency indicators, the optimal ESP operating frequency is determined to enhance offshore oilfield production. Adjusting the operating parameters based on these calculations enables continuous production optimization until the discrepancy between real-time data and computed results falls within the acceptable accuracy range. This method conserves substantial capital investment, enhances operational efficiency, and significantly boosts enterprise profitability.

关键词： Dynamic production optimization ESP performance analysis offshore oilfield production oil-water phases sequential quadratic programming virtual flow measurement

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Particle Swarm Optimization of Suction and Blowing on Airfoils at Transonic Speeds

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JOURNAL OF AIRCRAFT 2010年第6期47卷 1955-1965页

作者： Pehlivanoglu, Y. Volkan Yagiz, Bedri Kandil, O. Baysal, O. Turkish AF Acad TR-34149 Istanbul Turkey Old Dominion Univ Dept Aerosp Engn Norfolk VA 23529 USA Old Dominion Univ Batten Coll Engn & Technol Norfolk VA 23529 USA

To improve the aerodynamic performance, the control applied to the transonic flow past an airfoil is optimized. The flow is simulated by solving the Navier-Stokes equations and a turbulence model. The control is applied using flow suction or blowing on the airfoil surface. The design variables are set to be the location and the angle of the suction/blowing port and the mass flow coefficient at this port. To validate the computational methodology, successful comparisons have been made with available experimental data. The focus of the present study is on the application of a selected design optimization methodology. Therefore, the particle swarm optimization algorithm is also validated by comparing its results with those from a more traditional approach: that is, a gradient-based method with sequential quadratic programming. It is observed that the aerodynamic performance is increased by over 20% as a result of optimizing the location, angle, and mass flow coefficient of the suction and blowing control ports on the airfoil surface at transonic speeds.

关键词： Particle Swarm Optimization Supersonic Speed Aerodynamic Performance sequential quadratic programming CFD Mass Flow Reynolds Averaged Navier Stokes Aerodynamic Coefficients NACA 64A010 Coefficient of Performance

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