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检索条件"主题词=sequential Quadratic Programming"

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Rapid Trajectory Optimisation in Mars Pinpoint landing using Pseudo-Spectral method 5

Rapid Trajectory Optimisation in Mars Pinpoint landing using...

引用

5th Indian Control Conference (ICC)

作者： Swaminathan, Shriram Kumar, Sriram S. Indian Space Rasearch Org Vikram Sarabhai Space Ctr Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Dept Elect Engn CET Thiruvananthapuram Kerala India

ISBN: (纸本)9781538662465

Real time trajectory optimisation of powered descent phase in Mars pinpoint landing is presented in this paper. Pinpoint landing on Mars in the presence of large initial condition dispersions and constraints on thrust, orientation and path are solved in near real time. A new method for implementing Pseudo-Spectral based trajectory optimisation using Levenberg Marquardt algorithm, which is suitable for on board implementation is developed. The algorithm is highly insensitive to initial guess values, exhibiting very good convergence properties. The optimisation scheme is based on sequential quadratic programming. It is tested for robustness with respect to both initial guess and initial condition dispersions in the Monte-Carlo sense and the results are very encouraging.

关键词： Pseudo-Spectral Pinpoint Landing sequential quadratic programming Rapid Trajectory Optimisation Levenberg Marquardt Algorithm

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Objectives and Constraints for Transonic Wing Optimization

Objectives and Constraints for Transonic Wing Optimization

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Poole, D. J. Allen, C. B. Rendall, T. C. S. Univ Bristol Dept Aerosp Engn Bristol BS8 1TR Avon England Univ Bristol Dept Aerosp Engn Computat Aerodynam Bristol BS8 1TR Avon England

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Consideration of the aerodynamic shape optimization problem definition is presented. An issue with drag minimization is that shocked-free solutions often result which have isolated performance improvements. As such, in this paper, optimizing the range parameter is considered enriched with the operating point as a design variable. A constraint on dimensional lift is therefore required. Analytical treatment of this problem is used to demonstrate that supercritical solutions result providing the lift is above a critical threshold. Also, three-dimensional effects penalise lift coefficient and promote higher optimum Mach numbers. Single- and multi-point optimizations of the NASA CRM wing are considered, inspired by the AIAA ADODG Case 4, as well as a range optimization. A shocked solution is found for the range optimization.

关键词： Aerodynamic Shape Optimization Lift Coefficient Multi Point Optimization Reynolds Averaged Navier Stokes sequential quadratic programming Transonic Flow Aspect Ratio Wing Loading Computational Fluid Dynamics Aircraft Design

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Dual decomposition for QPs in scenario tree NMPC

Dual decomposition for QPs in scenario tree NMPC

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European Control Conference (ECC)

作者： Leidereiter, Conrad Potschka, Andreas Bock, Hans Georg Heidelberg Univ Interdisciplinary Ctr Sci Comp Neuenheimer Feld 368 D-69120 Heidelberg Germany

ISBN: (纸本)9783952426937

In the field of nonlinear model predictive control (NMPC) under uncertainty we use a robustification approach based on a scenario tree formulation. This approach is known to be less conservative than worst-case approaches. A main challenge of scenario tree NMPC in high-dimensional uncertainty spaces is the exponential growth of the number of scenarios for possible parameter realizations. Hence, the solution of the resulting optimization problem in every NMPC iteration becomes a bottleneck for the computation. We have to solve the problem with fast numerical methods to ensure real-time applicability of scenario tree NMPC. By a multiple shooting discretization we discretize the optimal control problems of an NMPC iteration to obtain a sequence of Nonlinear Programs that we solve by a real-time feasible variant of sequential quadratic programming (SQP). Every single quadratic programming problem (QP) exhibits a particular structure originating from the scenario tree. We use a dual decomposition approach on the non-anticipativity constraints from the scenario tree formulation to decouple the large-scale QP into many smaller QPs that each correspond to one scenario in the scenario tree. Within an outer level non-smooth Newton iteration in the dual space of the coupling constraints, the decoupled scenario QPs can be solved in a massively parallel fashion. In the final part of this contribution we present numerical results for NMPC with large scenario trees.

关键词： Newton method nonlinear control systems nonlinear programming optimal control predictive control quadratic programming robust control SQP coupling constraints dual decomposition high-dimensional uncertainty spaces multiple shooting discretization nonanticipativity constraints nonlinear model predictive control nonlinear program optimal control problems optimization problem outer level nonsmooth Newton iteration robustification approach scenario tree NMPC sequential quadratic programming worst-case approach Couplings Indexes Optimal control Optimization Real-time systems Robustness Uncertainty

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Modelling and optimization of residential heating system using Random Neural Networks

Modelling and optimization of residential heating system usi...

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IEEE International Conference on Control Science and Systems Engineering (CCSSE)

作者： Javed, Abbas Larijani, Hadi Ahmadinia, Ali Emmanuel, Rohinton Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow Lanark Scotland

ISBN: (纸本)9781479963973

In this paper, a novel random neural network (RNN) model based optimization process for radiator-based heating system is proposed to maintain a comfortable indoor environment in a living room of a single storey residential building. The predictive model of the living room is developed by training a feed forward RNN and then optimisation algorithms are used to calculate the optimal flowrate for the radiators. Three optimisation algorithms: Genetic Algorithm (GA), Particle swarm optimization (PSO) algorithm, and sequential quadratic programming (SQP) optimization algorithm are investigated to calculate the optimal control input. The accuracy of the control scheme is verified by simulations using International Building Physics Toolbox (IBPT). It was found that mean squared error (MSE) for PSO is 38.87% less than GA and the MSE for PSO is 21.19% less than SQP. The RNN model based optimization technique is further compared with model predictive controller (MPC) designed for the radiator based heating system. The comparison results showed that the proposed RNN technique minimize the energy consumption and maintains accurate room thermal comfort according to the predicted mean vote (PMV) based setpoints.

关键词： Random neural networks Particle Swarm Optimization Genetic Algorithm sequential quadratic programming

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Direct numerical solution of a time-optimal state-constrained control problem in a flow 7

Direct numerical solution of a time-optimal state-constraine...

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7th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Chertovskih, Roman Pereira, Fernando Lobo Univ Porto Fac Engn Res Ctr Syst & Technol SYSTEC Elect & Comp Engn Dept Porto Portugal

ISBN: (纸本)9781728159539

We consider the following time-optimal control problem with state constraints: compute minimal travelling time of a controllable object moving in a prescribed flow field in a bounded domain between two given points. The optimal control problem is solved numerically using two direct methods interior-point line search filter method and sequential quadratic programming. Five sample flows are considered, and computational properties of the corresponding simulations are measured and discussed.

关键词： optimal control direct methods interior-point method sequential quadratic programming

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Aerodynamic Shape Optimization for Unsteady Flows: Some Benchmark Problems

Aerodynamic Shape Optimization for Unsteady Flows: Some Benc...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Apponsah, Kwesi P. Zingg, David W. Univ Toronto Inst Aerosp Studies Toronto ON M3H 5T6 Canada Univ Toronto Inst Aerosp Studies Computat Aerodynam & Sustainable Aviat Toronto ON M3H 5T6 Canada Univ Toronto Inst Aerosp Studies Ctr Res Sustainable Aviat Toronto ON M3H 5T6 Canada Univ Toronto Inst Aerosp Studies Ctr Computat Sci & Engn Toronto ON M3H 5T6 Canada

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

We present an efficient aerodynamic shape optimization framework for optimization problems under unsteady flow conditions. The optimization framework consists of a parallel Newton-Krylov flow solver for multi-block grids and an integrated geometry parameterization and mesh-deformation algorithm based on linear elasticity. We apply the adjoint method to the discretized governing equations to compute the gradients required by the sequential quadratic programming optimization algorithm. We propose two lift-constrained drag minimization problems for the purposes of testing and evaluating the framework. First, we consider a laminar flow airfoil optimization problem at a Reynolds number of 800 and also investigate the convexity of the optimization problem. We show that the optimizer is capable of reducing the drag for this problem by about 23% and produces a nearly steady flow compared to the vortex shedding observed for the baseline geometry at the required lift target. The second benchmark case is a lift-constrained drag minimization of an aspect ratio eight rectangular wing in a laminar flow at a Reynolds number of 800. Section shape, twist, and angle of attack are free. Although only partially converged at the time of writing, the preliminary results show a 20% drag reduction.

关键词： Aerodynamic Shape Optimization Flow Conditions Lift Coefficient Airfoil Vortex Shedding sequential quadratic programming Aspect Ratio Lift to Drag Ratio Linear Elasticity Computational Fluid Dynamics

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Active-set Newton methods for mathematical programs with vanishing constraints

Active-set Newton methods for mathematical programs with van...

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Workshop on Non linear Optimization, Variational Inequalities and Equilibrium Problems

作者： Izmailov, A. F. Pogosyan, A. L. MSU OR Dept VMK Fac Moscow 119991 Russia

Mathematical programs with vanishing constraints constitute a new class of difficult optimization problems with important applications in optimal topology design of mechanical structures. Vanishing constraints usually violate standard constraint qualifications, which gives rise to serious difficulties in theoretical and numerical treatment of these problems. In this work, we suggest several globalization strategies for the active-set Newton-type methods developed earlier by the authors for this problem class, preserving superlinear convergence rate of these methods under weak assumptions. Preliminary numerical results demonstrate that our approach is rather promising and competitive with respect to the existing alternatives.

关键词： Mathematical program with vanishing constraints Constraint qualification Optimality condition Active-set method sequential quadratic programming Semismooth Newton method Global convergence

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Direct Methods Comparison for the Active Target Defense Scenario

Direct Methods Comparison for the Active Target Defense Scen...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Weintraub, Isaac E. Cobb, Richard Baker, William Pachter, Meir US Air Force Res Lab Wright Patterson AFB OH 45433 USA US Air Force Res Lab Dept Aerosp Engn Wright Patterson AFB OH 45433 USA US Air Force Res Lab Dept Mathemat Wright Patterson AFB OH 45433 USA US Air Force Res Lab Dept Elect Engn Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

The goal of optimal control is to obtain an admissible function which minimizes an objective functional subject to specified constraints and boundary conditions. Four popular methods of computing the optimal control directly are the Single Shooting Method, Multiple Shootings Method, Even Collocation Method, and Pseudospectral Method. In this paper, the four direct methods of computing the optimal control for the Active Target Defense Scenario are presented and compared. The Active Target Defense Scenario is a three-agent engagement where an evading agent tries to escape a pursuing agent with the assistance of a defending agent. The goal of the Evader is to maneuver in such a way as to assist the Defender in capturing the Pursuer while maximizing his range from the Pursuer. This work begins by describing each direct method and summarizing advantages and disadvantages of each. Next, each method is used to solve the optimal control for the Active Target Defense Scenario;results are presented and a comparison of the performance of each method is made. Finally, concluding remarks describing the results, performance, and real-time implementation are presented.

关键词： Optimal Control Theory Gaussian Quadrature Pseudospectral Methods Parallel Computing Techniques Nonlinear programming sequential quadratic programming Heading Angle Proportional Navigation Runge Kutta Methods MATLAB

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Designing a Hierarchical Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell System 19

Designing a Hierarchical Energy Management Strategy for a Hy...

引用

20th IEEE Vehicle Power and Propulsion Conference (IEEE VPPC)

作者： Kandidayeni, Mohsen Kelouwani, Sousso Boulon, Loic Trovao, Joao P. Univ Sherbrooke Dept Elect & Comp Engn Sherbrooke PQ Canada Univ Quebec Trois Rivieres Dept Elect & Comp Engn Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Dept Mech Engn Trois Rivieres PQ Canada Univ Sherbrooke E TESC Lab Sherbrooke PQ Canada IPC ISEC Coimbra Portugal INESC Coimbra Coimbra Portugal

ISBN: (纸本)9798350344455

This paper proposes a hierarchical energy management strategy (EMS) for a hybrid multi-stack fuel cell (FC) system. This system requires 2 kW of power from the FC side and the rest should be supplied by a lithium-ion battery pack. In the first step, several stack allocation schemes (dualstack, triple-stack, and quadruple-stack) are compared in terms of total system efficiency to provide the required power by the FCs and one of them is selected to be used in the hybrid system. Subsequently, the hierarchical EMS with two operating layers is deployed to perform the power allocation. In the first layer, the proposed EMS utilizes sequential quadratic programming to determine the portion of power that should be supplied by the multi-stack FC system while maintaining the battery state of charge within the accepted boundary limits. In the second layer, genetic algorithm is utilized to distribute the power among different FCs in the multi-stack system by maximizing an objective function based on efficiency of the whole system. The obtained results indicate that the proposed hierarchical EMS is able to decrease the hydrogen consumption up to 4.19% and 12.5% in comparison with other commonly used EMSs in the literature under the same driving cycle.

关键词： Multi-stack fuel cell energy management strategy genetic algorithm sequential quadratic programming

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Bi-objective Cross-Layer Design Using Different Optimization Methods in Multi-flow Ad-Hoc Networks 2nd

Bi-objective Cross-Layer Design Using Different Optimization...

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2nd International Conference on Information, Communication and Computing Technology (ICICCT)

作者： Mehta, Ridhima Lobiyal, D. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

ISBN: (纸本)9789811065446;9789811065439

In this paper, we employ three different optimization methods to solve a bi-objective cross-layer design problem in ad-hoc networks. These methods are active-set, interior-point and sequential quadratic programming (SQP) methods. Specifically, we formulate the proposed problem as a nonlinear optimization problem subject to the underlying network operating conditions. The two objectives considered for optimization include minimizing the aggregate link powers and maximizing the overall network utility. For this, two different ad-hoc topology scenarios with multi-flow network design are used to implement the proposed problem. The simulation results demonstrate the convergence process of various iterative methods to their optimal solutions within finite number of iterations. The maximum convergence rate achieved in our scheme is as high as 76.5%.

关键词： Active-set Ad-hoc network Cross-layer design Interior-point sequential quadratic programming

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