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2nd IEEE International Conference on Emerging Smart Computing and Informatics (ESCI)

作者： Kumar, Prashant Sonkar, Sarvesh Ghosh, A. K. Philip, Deepu IIT Kanpur Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781728152639

The paper describes the methodology and the design process of a Model Predictive Controller (MPC) that has been applied to solve Missile-on-Missile Interception. The MPC combines the control and guidance laws to create an ideal control law with much higher performance and guarantees strike with a wide range of target and missile parameters. The MPC is also able to handle actuator delay and hence is more practically suited than traditional guidance/control schemes. Also, MPC satisfies sensor/seeker angle constraints, actuator constraints, angle of attack constraints, and fin deflection angle constraints, thus making it more versatile and powerful than any analytical method-based control/guidance schemes. The only disadvantage is the higher computational cost requirements, which will be easily mitigated in the future with the advancements of powerful microprocessors. The performance of the MPC is demonstrated in simulations where the missile is a standard supersonic missile but the target is a maneuvering hypersonic missile with initial separation of just a few kilometers.

关键词： Model predictive Control Nonlinear constraints sequential quadratic programming Gradient Step

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Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO

Bilevel Multi-objective Optimization Problem Solving Using P...

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6th International Conference on Evolutionary Multi-Criterion Optimization

作者： Sinha, Ankur Aalto Univ Sch Econ Dept Business Technol FIN-00076 Helsinki Finland

ISBN: (纸本)9783642198922

Bilevel multi-objective optimization problems are known to be highly complex optimization tasks which require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. Multi-objective bilevel problems are commonly found in practice and high computation cost needed to solve such problems motivates to use multi-criterion decision making ideas to efficiently handle such problems. Multi-objective bilevel problems have been previously handled using an evolutionary multi-objective optimization (EMO) algorithm where the entire Pareto set is produced. In order to save the computational expense, a progressively interactive EMO for bilevel problems has been presented where preference information from the decision maker at the upper level of the bilevel problem is used to guide the algorithm towards the most preferred solution (a single solution point). The procedure has been evaluated on a set of five DS test problems suggested by Deb and Sinha. A comparison for the number of function evaluations has been done with a recently suggested Hybrid Bilevel Evolutionary Multi-objective Optimization algorithm which produces the entire upper level Pareto-front for a bilevel problem.

关键词： Genetic algorithms evolutionary algorithms bilevel optimization multi-objective optimization evolutionary programming multi-criteria decision making hybrid evolutionary algorithms sequential quadratic programming

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Multi-fidelity Constrained Optimization Methods Applied to Benchmark Problems

Multi-fidelity Constrained Optimization Methods Applied to B...

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AIAA SciTech Forum

作者： Rumpfkeil, M. P. Serani, A. Beran, Philip S. Univ Dayton Dept Mech & Aerosp Engn Dayton OH 45469 USA CNR INM Inst Marine Engn Rome Italy Air Force Res Lab Dayton OH USA INM Rome Via Vallerano 139 I-00128 Rome Italy

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The traditional design process relies upon low-fidelity models for expedience and resource savings. However, the reduced accuracy and reliability of low-fidelity tools often lead to the discovery of design defects or inadequacies late in the design process. These deficiencies result either in costly changes or the acceptance of configurations that do not meet expectations. Multi-fidelity methods attempt to blend the increased accuracy and reliability of high-fidelity models with the reduced cost of low-fidelity models. In this paper, two multi-fidelity constrained optimization approaches are presented and exercised on two analytical benchmark problems as well as a constrained drag minimization problem for the RAE-2822 airfoil. The results show promise in overall computational savings compared to using high-fidelity information alone.

关键词： sequential quadratic programming RAE airfoil Computational Modeling Design Optimization Drag Coefficient Lift Coefficient Particle Swarm Optimization Central Composite Designs Taylor Series Thickness to Chord Ratio

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A new SQP based space-mapping algorithm for on-chip spiral inductor optimization

A new SQP based space-mapping algorithm for on-chip spiral i...

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20th Annual Canadian Conference on Electrical and Computer Engineering

作者： Arbabi, Navid Najmabadi, Mani Devabhaktuni, Vijay Yagoub, Mustapha Concordia Univ Dept ECE Montreal PQ Canada Univ Ottawa SITE Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781424410200

Computer aided design (CAD) and optimization of on-chip spiral inductors is of immense interest to RFIC designers. Traditional optimization methods rely on approximate/fast "coarse models" of spiral inductors, eg. equivalent circuits, empirical equations, etc. Naturally, the accuracy/validity of optimized results from such methods depends highly on the quality of the coarse models used. Further, the inability of such models to account for full-wave electromagnetic (EM) effects, limits their application to low-frequency circuit designs and/or necessitates additional efforts to formulate an acceptable optimization objective. The pressing need for first-pass success of CAD tools in terms of inductor design mandates consideration of full-wave EM effects within the CAD framework. Space-mapping optimization combines the speed of circuit models and the accuracy of EM simulations. In this paper, we propose a new space-mapping optimization algorithm, which exploits the sequential quadratic programming (SQP) technique for coarse model optimization. Starting with the user-specifications of the spiral inductor, the proposed algorithm yields an acceptable spiral inductor design/layout for RFIC design. The algorithm is illustrated via an on-chip spiral inductor optimization example.

关键词： computer aided design EM optimization RFICs space-mapping sprial inductors sequential quadratic programming

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Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC 7

Uncertainty Propagation by Linear Regression Kalman Filters ...

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7th IFAC Conference on Nonlinear Model Predictive Control (NMPC)

作者： Quirynen, Rien Berntorp, Karl Mitsubishi Elect Res Labs Cambridge MA 02139 USA

Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximation of the stochastic optimal control problem. In addition, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is presented to considerably reduce the computational cost and allow a real-time implementation of the resulting SNMPC. The prediction accuracy and control performance of the proposed approach are illustrated on a vehicle control application subject to external disturbances, while highlighting a worst-case computation time of 10 ms for SNMPC which is close to that of deterministic NMPC for this particular case study. Copyright (C) 2021 The Authors.

关键词： Stochastic nonlinear model predictive control linear-regression Kalman filtering sequential quadratic programming real-time optimization algorithms

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Optimal Operation of Multiple Unbalanced Distributed Generation Sources in Three-Phase Four-Wire LV Distribution Networks

Optimal Operation of Multiple Unbalanced Distributed Generat...

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22nd Australasian Universities Power Engineering Conference (AUPEC) - Green Smart Grid Systems

作者： Su, Xiangjing Wolfs, Peter Masoum, Mohammad A. S. Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9789791884723

Distributed Generation (DG) in the form of residential roof top photovoltaic installations is driven by consumer action. The placement of DG in the distribution network is not controlled by the network operator. Power quality issues, especially voltage rise and unbalance, is restricting ability of networks to accommodate further connections. There is a growing interest in utilizing the latent capacity of DG inverters to provide reactive power, or to integrate storage into DG systems, to increase the renewable power fraction. This paper presents an optimization method that is able to simultaneously manage the operation of many arbitrary located residential DG sources to reduce system losses and improve power quality. The optimization model is solved by a sequential quadratic programming (SQP) based approach and the validity is tested on an accurate three-phase four-wire unbalanced distribution network model developed during the Perth Solar City trial.

关键词： sequential quadratic programming distribution network three-phase four-wire balance

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Parameter Identification for a 3D Heating Process by the Adjoint Method based on Experimental Data 6th

Parameter Identification for a 3D Heating Process by the Adj...

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6th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： Wolfram, Dirk Meurer, Thomas Univ Kiel Fac Engn Chair Automat Control D-24143 Kiel Germany

Model-based control of systems governed by partial differential equations relies on the knowledge of the model parameters. Their determination is complicated by the spatial-temporal process dynamics. In addition the interaction of the process and the (embedded) actuation devices might be subject to uncertainties. This work applies a late-lumping approach for parameter identification given a 3-dimensional heat conduction and heat transfer problem with actuation dynamics represented by a coupled PDE-ODE model. By defining a suitable minimization problem the necessary optimality conditions in terms of adjoint PDE-ODE couplings are determined using variational calculus. In additional gradient information can be directly extracted that is used in course of the numerical evaluation by making use of sequential quadratic programming. For this the Finite-Element method is applied for the forward solution of the model equations and backward solution of the adjoint equations. Data collected at the experimental realization of 3-dimensional heating process for different actuation scenarios is used for evaluation and comparison. Copyright (C) 2021 The Authors.

关键词： Parameter identification Heat equation Adjoint Method Actuator dynamics Optimization Finite Element Method Partial Differential Equations sequential quadratic programming

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The Lagrange-Newton method for state constrained optimal control problems

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Computational Optimization and Applications 1995年第3期4卷 217-239页

作者： Alt, Walter Malanowski, Kazimierz Mathematisches Institut Universität Bayreuth Bayreuth D-95440 Germany Systems Research Institute Polish Academy of Sciences Warszawa 01-447 ul. Newelska 6 Poland

Local convergence of the Lagrange-Newton method for optimization problems with two-norm discrepancy in abstract Banach spaces is investigated. Based on stability analysis of optimization problems with two-norm discrepancy, sufficient conditions for local superlinear convergence are derived. The abstract results are applied to optimal control problems for nonlinear ordinary differential equations subject to control and state constraints. © 1995 Kluwer Academic Publishers.

关键词： AMS subject classification: 49M05, 49M15, 65K05 Lagrange-Newton method nonlinear ordinary differential equations optimal control sequential quadratic programming state constraints two-norm discrepancy

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Structural Topology Optimisation with R-Snakes Volume of Solid

Structural Topology Optimisation with R-Snakes Volume of Sol...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Taylor, Alexander D. J. Payot, Alexandre D. J. Allen, C. B. Rendall, Thomas 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)9781624105784

ISBN: (纸本)9781624105784

A topologically flexible parameterisation method developed for aerodynamic optimisation is tested on a variety of 2 dimensional structural topology problems. The background to the restricted snakes volume of solid (RSVS) is explained. A topology optimisation framework is develloped using FreeFem++ as a linear elastic solver and differential evolution as the agent based optimiser. Validation of the analysis is presented as well as mesh convergence and impact studies. The work here is significant such that if a parameterisation method is capable of both aerodynamic and structural optimisation, the potential for multidisciplinary analysis arises. Structural topology optimisation of volume constrained agent-based optimisation, minimising deflection, has been run on four common benchmark cases;two short cantilever beams and two longer centrally loaded (MBB) beams. Results were compared to SIMP calculations made following using Sigmund's 99 line topology optimisation code [1]. RSVS solutions scored objective functions comparable to those found via SIMP in all cases with differences ranging from +40% to -5%. In the long run the combined capability of the RSVS in aerodynamics and structures offers the potential of the development of a general aero-structural topology optimisation framework using a single set of design variables.

关键词： Numerical Optimization Structural Optimization Cantilever Beam Aerodynamic Shape Optimization sequential quadratic programming Aspect Ratio Biaxial Stress Finite Element Analysis Poisson's Ratio Aircraft Configurations

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Multilevel parallel GPU implementation of SQP solvers for Nonlinear MPC

Multilevel parallel GPU implementation of SQP solvers for No...

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European Control Conference (ECC)

作者： Verheijen, P. C. N. Derkani, A. H. Agarwal, Y. A. Lazar, M. Goswami, D. Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9798331540920;9783907144107

In recent literature, it has been shown that the number of steps in a sequential quadratic programming algorithm for a non-linear model predictive control (NMPC) problem can be greatly reduced by a parallel shooting method. The efficiency of such a parallel shooting method further depends on how the algorithm is implemented on parallel computing platforms such as Graphics Processing Units (GPUs). The GPU implementation should consider the degree of parallelism necessary for higher time efficiency as well as the hardware resource consumption/limitation at the GPU for a given problem size. In this paper, we present a multilevel parallel GPU implementation for sequential quadratic programming and an (Alternating Direction Method of Multipliers) ADMM solver. First, we introduce a GPU implementation enabling parallel computing of many quadratic programs (QPs) by functional parallelism. Next, we parallelize each QP solver using data parallelism of basic linear matrix operations. We show that the proposed GPU implementation greatly scales with the degree of parallelism in the parallel shooting method. Further, we show how a GPU implementation can be configured for a given problem size avoiding resource overprovisioning.

关键词： Nonlinear MPC sequential quadratic programming CUDA programming Parallel Shooting

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