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A novel nonlinear model predictive control design based on a hybrid particle swarm optimization-sequential quadratic programming algorithm: application to an evaporator system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2016年第1期38卷 23-32页

作者： Rajabi, Farshad Rezaie, Behrooz Rahmani, Zahra Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

This paper proposes a hybrid algorithm by combining particle swarm optimization (PSO) with sequential quadratic programming (SQP) to handle the optimization part of the nonlinear model predictive control (NMPC). In the proposed method, a nonlinear model of the plant is directly applied to predict the future behaviour of the system for a certain horizon. In each sampling time a constrained nonlinear optimization (CNO) problem has to be solved in order to find the optimizing input sequence with predefined length. These solutions minimize the errors between the predicted states and the desired ones of the system. Only the first optimizing input is applied to the system and the rest of the sequence is discarded. The whole prediction and optimization must be repeated for the next upcoming steps. The evolutionary and heuristic PSO algorithm in cooperation with the powerful local minimizing SQP algorithm can quickly find these optimizing solutions and also can satisfy the constrains. The evaporation process due to its nonlinear dynamics and the existence of disturbances affecting the evaporator is considered to evaluate the performance of the proposed method. The simulation results show that the controller designed via the proposed approach can make the system track the reference commands and satisfy the restrictions properly. Moreover, based on the simulation results it can be seen that this approach is more efficient in comparison with the NMPC method, in which only the SQP algorithm has been utilized for optimization, and also the linear model predictive control method.

关键词： Linear model predictive control nonlinear model predictive control constrained nonlinear optimization particle swarm optimization sequential quadratic programming evaporator

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Skobelev network optimisation by sequential quadratic programming

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ELECTRONICS LETTERS 2010年第8期46卷 553-U22页

作者： Petrolati, D. Angeletti, P. Morini, A. Toso, G. European Space Agcy NL-2200 AG Noordwijk Netherlands Univ Politecn Marche I-60131 Ancona Italy

A new method for optimising the directional couplers characterising a Skobelev network is presented. The method adopted succeeds in improving the performances of the related sub-array radiation patterns in terms of si... 详细信息

关键词： Skobelev network optimisation antenna arrays antenna radiation patterns antenna theory directional couplers directional couplers quadratic programming sequential quadratic programming sidelobe level subarray radiation patterns

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A sequential quadratic programming method for contingency-constrained phasor measurement unit placement

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第12期25卷 3185-3211页

作者： Theodorakatos, Nikolaos P. Manousakis, Nikolaos M. Korres, George N. NTUA Sch Elect & Comp Engn Athens 15780 Greece

The paper proposes a multi-objective based optimization problem to design the optimal placement of phasor measurement units (PMUs), which make the power system network completely observable. The optimization process tries to attain dual objectives: (i) to minimize the total number of PMUs required and (ii) to maximize the measurement redundancy at all buses in a power system. A sequential quadratic programming algorithm is used to determine the number of PMUs and their optimal locations. Existing conventional measurements and the limited PMU channel capacity can also be incorporated in the proposed PMU placement formulation. When a system is made observable with a minimum number of PMUs, lack of communication facilities in substations or a PMU loss will lead to unobservable buses in the power system. Hence, the communication constraints and loss of a PMU have to be considered in the design stage. The proposed method is successfully applied to IEEE test systems in MATLAB, and the simulation results are presented. The simulation results are compared with a binary integer linear programming (BILP) model, also implemented in MATLAB, in order to demonstrate the effectiveness and accuracy of the proposed methodology. The comparative study shows that the proposed model yields the same number of PMUs as the optimal one found by the BILP model for each case study. The advantage of the proposed optimization scheme is that, starting from an initial point, the method is able to yield different PMU placement sets each one having the same minimum number of PMUs, for each case study. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： phasor measurement unit observability optimal PMU placement optimization integer linear programming sequential quadratic programming

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Robust topology optimization formulation including local failure and load uncertainty using sequential quadratic programming

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INTERNATIONAL JOURNAL OF MECHANICS AND MATERIALS IN DESIGN 2019年第2期15卷 317-332页

作者： Long, Kai Wang, Xuan Du, Yixian North China Elect Power Univ Beijing Key Lab Energy Safety & Clean Utilizat Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Changsha Univ Sci & Technol Educ Dept Hunan Prov Key Lab Safety Design & Reliabil Technol Engn Veh Changsha 410114 Hunan Peoples R China Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China China Three Gorges Univ Coll Mech & Power Engn Yichang 443002 Peoples R China

This paper introduces a new formulation of topology optimization for robust design including local failure and load uncertainty. In contrast to most studies, the focus has been on minimizing the total volume with multiple compliance constraints. With the introduction of the reciprocal intermediate variables, the topology optimization problem can be well posed as a sequential quadratic program with exact second-order information. Then, robust topology optimization is implemented within the framework of the suggested formulation, in which not only the randomness of the damage location but also the uncertainty of loading magnitude and direction are taken into account. Finally, several numerical examples are performed to verify the effectiveness and capability of the presented approach for robust design considering local failure and load uncertainty. The effects that varying the input load magnitude and direction, damage location have upon the optimized designs are investigated by comparing those with deterministic design results.

关键词： Topology optimization Reciprocal SIMP method sequential quadratic programming Load uncertainty Fail-safe design

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Force control of two-wheel-steer four-wheel-drive vehicles using model predictive control and sequential quadratic programming for improved path tracking

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第6期14卷

作者： Tan, Qifan Wang, Xu Taghia, Javad Katupitiya, Jayantha Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Univ New South Wales Sch Mech & Mfg Engn Kensington NSW Australia

This article proposes a novel approach for the two-wheel-steer four-wheel-drive vehicle path tracking based on force control. Both the kinematic and dynamic models are used to calculate the virtual inputs based on a model predictive control algorithm. By using these virtual inputs as the constraints of a real-time sequential quadratic programming optimization, the optimal real inputs such as the drive forces and the steering angles are obtained. Note that the system is discretized when designing the control algorithm. The novelty is that all the forces are taken into account in the dynamic model, and the controller uses optimal steering angles and drive forces to obtain the minimum path offset. The integrated algorithms are then used in tracking a predefined path. Simulation results are provided to demonstrate the effectiveness and applicability of the proposed approach.

关键词： Ground vehicle force control path tracking model predictive control sequential quadratic programming

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Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming

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MATHEMATICAL programming 2023年第1-2期202卷 279-353页

作者： Na, Sen Anitescu, Mihai Kolar, Mladen Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Int Comp Sci Inst Berkeley CA 94704 USA Argonne Natl Lab Math & Comp Sci Div Argonne WI USA Univ Chicago Booth Sch Business Chicago IL USA

We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks. We propose an active-set stochastic sequential quadratic programming (StoSQP) algorithm that utilizes a differentiable exact augmented Lagrangian as the merit function. The algorithm adaptively selects the penalty parameters of the augmented Lagrangian, and performs a stochastic line search to decide the stepsize. The global convergence is established: for any initialization, the KKT residuals converge to zero almost surely. Our algorithm and analysis further develop the prior work of Na et al. (Math Program, 2022. https://***/10.1007/s10107-022-01846-z). Specifically, we allow nonlinear inequality constraints without requiring the strict complementary condition;refine some of designs in Na et al. (2022) such as the feasibility error condition and the monotonically increasing sample size;strengthen the global convergence guarantee;and improve the sample complexity on the objective Hessian. We demonstrate the performance of the designed algorithm on a subset of nonlinear problems collected in CUTEst test set and on constrained logistic regression problems.

关键词： Inequality constraints Stochastic optimization Exact augmented Lagrangian sequential quadratic programming

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An integrated perturbation analysis and sequential quadratic programming approach for Model Predictive Control

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AUTOMATICA 2009年第10期45卷 2412-2418页

作者： Ghaemi, Reza Sun, Jing Kolmanovsky, Ilya V. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA Ford Motor Co Dearborn MI 48124 USA

Computationally efficient algorithms are critical in making Model Predictive Control (MPC) applicable to broader classes of systems with fast dynamics and limited computational resources. In this paper, we propose an integrated formulation of Perturbation Analysis and sequential quadratic programming (InPA-SQP) to address the constrained optimal control problems. The proposed algorithm combines the complementary features of perturbation analysis and SQP in a single unified framework, thereby leading to improved computational efficiency and convergence property. A numerical example is reported to illustrate the proposed method and its computational effectiveness. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Nonlinear MPC Fast MPC Neighboring Extremal Optimal Control sequential quadratic programming

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OPTIMUM DESIGN OF SYSTEMS FOR DYNAMICS AND CONTROLS USING sequential quadratic-programming

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AIAA JOURNAL 1989年第12期27卷 1793-1800页

作者： TSENG, CH ARORA, JS UNIV IOWA COLL ENGNIOWA CITYIA 52242

This paper describes application of a sequential quadratic programming method for dynamic response optimization and optimal control problems. Several methods to treat time-dependent constraints are described. Numerical procedures to solve this class of problems are studied and promising ones are implemented. They are evaluated by solving numerous problems. Numerical experience with the solution procedures is described and conclusions that can be useful for practical applications are given. © 1989 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

关键词： sequential quadratic programming Structural Dynamics Time Optimal Control Nonlinear programming Sensitivity Analysis Numerical Integration Spline Interpolation Computer Memory Modal Analysis Finite Difference Approximation

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Stress-constrained topology optimization of continuum structures subjected to harmonic force excitation using sequential quadratic programming

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2019年第5期59卷 1747-1759页

作者： Long, Kai Wang, Xuan Liu, Hongliang North China Elect Power Univ Beijing Key Lab Energy Safety & Clean Utilizat Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

In this paper, we propose a method for stress-constrained topology optimization of continuum structure sustaining harmonic load excitation using the reciprocal variables. In the optimization formulation, the total volume is minimized with a given stress amplitude constraint. The p-norm aggregation function is adopted to treat the vast number of local constraints imposed on all elements. In contrast to previous studies, the optimization problem is well posed as a quadratic program with second-order sensitivities, which can be solved efficiently by sequential quadratic programming. Several numerical examples demonstrate the validity of the presented method, in which the stress constrained designs are compared with traditional stiffness-based designs to illustrate the merit of considering stress constraints. It is observed that the proposed approach produces solutions that reduce stress concentration at the critical stress areas. The influences of varying excitation frequencies, damping coefficient and force amplitude on the optimized results are investigated, and also demonstrate that the consideration of stress-amplitude constraints in resonant structures is indispensable.

关键词： Topology optimization Harmonic analysis p-norm Solid isotropic material with penalization Stress constraints sequential quadratic programming

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Optimization of a multiproduct economic production quantity problem with stochastic constraints using sequential quadratic programming

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KNOWLEDGE-BASED SYSTEMS 2015年 84卷 98-107页

作者： Pasandideh, Seyed Hamid Reza Niaki, Seyed Taghi Akhavan Gharaei, Abolfazl Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

In this paper, a multiproduct single vendor-single buyer supply chain problem is investigated based on the economic production quantity model developed for the buyer to minimize the inventory cost. The model to be more applicable for real-world supply chain problems contains five stochastic constraints including backordering cost, space, ordering, procurement, and available budget. The objective is to find the optimal order quantities of the products such that the total inventory cost is minimized while the constraints are satisfied. The recently-developed sequential quadratic programming (SQP), as one of the best optimization methods available in the literature, is used to solve the problem. Twenty numerical examples in 3 scales of small, medium, and large are solved in order to demonstrate the applicability of the proposed methodology and to evaluate its optimum performance. The results show that SQP has satisfactory performance in terms of optimum solutions, number Of iterations to achieve the optimum solution, infeasibility, optimality error, and complementarity. Besides, the optimum performance of the SQP method is compared with the one of another exact method called interior point using the above numerical examples under similar conditions. The comparison results are in favor of the employed SQP. At the end, a sensitivity analysis is performed on the change rate of the objective function obtained based on the change rate of the variance of the order quantity. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Economic production quantity Stochastic constraints sequential quadratic programming Interior point method Optimization

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