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The Third International Conference on Dynamics, Vibration and Control

作者： Zhizhong.Zhao~1,Yan.Liu~1,Shiwei,Yan~1,Zhenghong.Gao~1 1 National Key Laboratory of Aerodynamics Design and Research,Northwestern Polytechnical University,Xi'an 710072,China

Time Domain Neal-Smith(TDNS) criterion is an ideal method for evaluation PIO *** characteristic of pilot's self-adapting make it difficult to apply the *** this *** quadratic programming(SQP) algorithm is used for TDNS criterion,and the PIO susceptibility of an example aircraft is *** evaluation results indicate that it is practicable to evaluate PIO susceptibility by TDNS criterion with SQP algorithm.

关键词： sequential quadratic programming PIO susceptibility time domain Neal-Smith criterion time domain evaluation

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A New Robust Load Frequency Control Design Using sequential quadratic programming (SQP) Method

A New Robust Load Frequency Control Design Using Sequential ...

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IEEE Mediterranean Electrotechnical Conference

作者： M. Ezatabadi Pour A. Khodabaklishian Electrical Engineering Department University of Isfahan

ISBN: (纸本)9781424457939

This paper presents a new technique for tuning the parameters of a PID controller for load frequency control (LFC) using sequential quadratic programming (SQP) method. In this method the frequency deviation of the system is directly utilized to tune the controller parameters. Simulations are carried out with considering the effect of generation rate constraints (GRC) and the governor limiters. Comparative results of the proposed method and a conventional PI controller show its robustness with a satisfactory response when the parameters of the system change.

关键词： new technique Proportional Integral Derivative Control tuning load frequency control PI control sequential quadratic programming GRC Frequency deviation

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Model Predictive Control for Constrained Discrete Time Systems: An Integrated Perturbation Analysis and sequential quadratic programming Approach

Model Predictive Control for Constrained Discrete Time Syste...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 2759-3219

作者： Reza Ghaemi Jing Sun Ilya Kolmanovsky Department of Electrical Engineering and Computer Science and the Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA University of Michigan USA

ISBN: (纸本)9781424414970;1424414970

Further improvements in computational efficiency of numerical optimization algorithms is a promising venue to extend the applicability of Model Predictive Control (MPC) to broader classes of embedded systems with fast dynamics and limited computing resources. Along these lines, we develop a novel numerical optimization algorithm based on Integrated Perturbation Analysis and sequential quadratic programming (IPA-SQP), which exploits special structure of the optimization problem and complementary features of Perturbation Analysis and SQP methods, to improve computational efficiency in general MPC problems with mixed state and input constraints. An example is reported to illustrate the reduction in on-line computing time achieved with IPA-SQP approach.

关键词： Optimization algorithms sequential quadratic programming Computational efficiency Model predictive control Predictive control perturbation discrete time systems Structural optimization Special structures vortex state (superconductivity) Maximum Allowable Concentration Embedded systems quadratic programming

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Varying-Radius Tunnel-Following NMPC for Robot Manipulators Using sequential Convex quadratic programming 2nd

Varying-Radius Tunnel-Following NMPC for Robot Manipulators ...

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2nd Modeling, Estimation and Control Conference (MECC)

作者： Astudillo, Alejandro Pipeleers, Goele Gillis, Joris Decre, Wilm Swevers, Jan Katholieke Univ Leuven MECO Res Team Dept Mech Engn DMMS Lab B-3001 Leuven Belgium

The tunnel-following nonlinear model predictive control (NMPC) scheme for robot manipulators allows the definition of tasks where deviations from a given path reference are allowed but upper-bounded by a user-defined parameter, which for a position tunnel represents the radius of the tunnel. The underlying optimal control problem (OCP) in this scheme can be efficiently solved by using the sequential convex quadratic programming (SCQP) method. Up to now, this scheme has been implemented with constant tunnel radii, although several tasks, such as human-robot collaboration or pick-and-place tasks, would benefit from variable radii throughout task execution. The SCQP method is however not able to exploit the structure of varying-radius tunnel constraints, which can lead to unstable iterations of the SQP method. In this work, we propose a reformulation of the tunnel constraints to overcome this issue, allowing the use of the SCQP method to efficiently solve the underlying OCP. We also provide insight into an efficient implementation of the SCQP method using the lin operator and prove the main theorem underlying such operator. Simulation results of a task involving a varying-radius tunnel are presented to support the applicability of the proposed methods. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Optimal control robot manipulators model predictive control tunnel following sequential quadratic programming

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Power Efficient Resource Allocation Algorithm based on sequential quadratic programming in Dense Femtocell Deployment

Power Efficient Resource Allocation Algorithm based on Seque...

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IET Intelligent Signal Processing Conference

作者： Fei Peng Yue Gao Laurie Cuthbert School of Electronic Engineering and Computer Scienece Queen Mary University of London

ISBN: (纸本)9781629938431

In dense deployment, Femtocells can be deployed in very close proximity in apartments. In OFDMA (Orthogonal Frequency Division Multiplexing Access) systems, this dense deployment may lead to severe co-tier interference between Femtocells when they are operating in the same sub-channels. In this paper, a power efficient resource allocation algorithm based on sequential quadratic programming (SQP) is proposed to mitigate co-tier interference in dense Femtocell deployment. This scheme aims at maximizing the throughput of Femtocells while limiting the co-tier interference among Femtocells, and guaranteeing the minimum Signal to Interference plus Noise Ratio (SINR) requirement of Femto User Equipments (Femto UEs). A two-tier OFDMA Femtocell simulator is established to demonstrate the system performance.

关键词： Dense Femtocell Deployment Resource Allocation sequential quadratic programming Power Efficient

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A sensitivity result for quadratic semidefinite programs with an application to a sequential quadratic semidefinite programming algorithm

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COMPUTATIONAL & APPLIED MATHEMATICS 2012年第1期31卷 205-218页

作者： Garces, Rodrigo Gomez, Walter Jarre, Florian Univ La Frontera Dept Engn Math Temuco 01145 Chile EuroAmerica SA Santiago Chile Univ Dusseldorf Inst Math D-40225 Dusseldorf Germany

In this short note a sensitivity result for quadratic semidefinite programming is presented under a weak form of second order sufficient condition. Based on this result, also the local convergence of a sequential quad... 详细信息

关键词： semidefinite programming second order sufficient condition sequential quadratic programming quadratic semidefinite programming sensitivity convergence

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Varying-Radius Tunnel-Following NMPC for Robot Manipulators Using sequential Convex quadratic programming

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IFAC-PapersOnLine 2022年第37期55卷 345-352页

作者： Alejandro Astudillo Goele Pipeleers Joris Gillis Wilm Decré Jan Swevers MECO Research Team Dept. of Mechanical Engineering KU Leuven. DMMS Lab Flanders Make 3001 Leuven Belgium

关键词： Optimal control robot manipulators model predictive control tunnel following sequential quadratic programming

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A sequential generalized quadratic programming algorithm using exactL1Penalty Functions∗

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Optimization Methods and Software 1993年第2期2卷 145-176页

A sequential generalized quadratic programming algorithm for nonlinear programming problems with equality and inequality constraints that uses anl1penalty function is described. The algorithm incorporates an automatic adjustment rule for the selection of two penalty parameters and uses an Armijo-type line search procedure and second order corrections. Global convergence and local superlinear convergence results are proved. Numerical results are given to illustrate the performance of the algorithm.

关键词： Nonlinear programming sequential quadratic programming Kuhn–Tucker point local and global convergence superlinear convergence Maratos effect

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sequential ℓ 1 quadratic programming for Nonlinear Model Predictive Control

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IFAC-PapersOnLine 2019年第1期52卷 474-479页

作者： Dimitri Boiroux John Bagterp Jørgensen Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

In this paper, we present and describe a computationally efficient sequential l1 quadratic programming (Sl1QP) algorithm for Nonlinear Model Predictive Control (NMPC). We use a tailored trust region sequential quadratic programming for the solution of the optimal control problem (OCP) involved in the NMPC algorithm. We use a multiple shooting approach for numerical integration and sensitivity computation. A second order correction ensures a faster convergence of the SQP algorithm. We exploit the structure of the OCP by using an efficient primal-dual interior point algorithm based on Riccati factorizations and a block diagonal BFGS update of the Hessian matrix. The complexity scales linearly with the prediction horizon length. We numerically evaluate and compare the performance of our algorithm on a numerical example.

关键词： Nonlinear model predictive control trust region algorithm sequential quadratic programming

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AN ACTIVE-SET METHOD FOR quadratic programming BASED ON sequential HOT-STARTS

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SIAM JOURNAL ON OPTIMIZATION 2015年第2期25卷 967-994页

作者： Johnson, Travis C. Kirches, Christian Waechter, Andreas Northwestern Univ Deptartment Engn Sci & Appl Math Evanston IL 60208 USA Heidelberg Univ Interdisciplinary Ctr Sci Comp IWR D-69120 Heidelberg Germany Northwestern Univ Deptartment Ind Engn & Management Sci Evanston IL 60208 USA

A new method for solving sequences of quadratic programs (QPs) is presented. For each new QP in the sequence, the method utilizes hot-starts that employ information computed by an active-set QP solver during the solution of the first QP. This avoids the computation and factorization of the full constraint and Hessian matrices for all but the first problem in the sequence. The proposed algorithm can be seen as an extension of the iterative refinement procedure for linear systems to QP problems, coupled with the application of an accelerated linear solver method that employs hot-started QP solves as a preconditioner. Local convergence results are presented. The practical performance of the proposed method is demonstrated on a sequence of QPs arising in nonlinear model predictive control and during the solution of a set of randomly generated nonlinear optimization problems using sequential quadratic programming. In these experiments, the method proves to be fairly reliable, despite the lack of global convergence guarantees. The results also show a significant reduction in the computation time for large problems with dense constraint matrices, as well as in the number of matrix-vector products.

关键词： nonlinear programming quadratic programming active set hot-starts iterative linear solver preconditioner sequential quadratic programming nonlinear model predictive control

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