Recently, it has been shown that taking the total velocity characteristic, the time of flight, and the trajectory safety into consideration and constructing a multi-objective optimization problem is an attractive and ...
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Recently, it has been shown that taking the total velocity characteristic, the time of flight, and the trajectory safety into consideration and constructing a multi-objective optimization problem is an attractive and realistic proposition for rendezvous trajectory design. Luo et al. [1] formulated the multi-objective linearized rendezvous optimization problem and solved it through the multi-objective genetic algorithm NSGA-II. It was shown that the tradeoffs between time of flight, propellant cost, and trajectory safety are quickly established using NSGA-II. In recognition of the drawbacks associated with linearized rendezvous equations, this study was expanded to a nonlinear two-body rendezvous by using NSGA-II and a Lambert algorithm. The nonlinear two-body multi-objective model is more accurate and suitable for more problems in comparison with linearized rendezvous models, which are limited to circular and near-rendezvous. However, the two-body model still does not take into account trajectory perturbations, such as nonspherical perturbations and atmospheric drag, which exist in real operational missions. Thus, it is desirable to be able to obtain Pareto-optimal solutions for perturbed rendezvous trajectories.
An algorithm for smooth nonlinear constrained optimization problems is described, in which a sequence of feasible iterates is generated by solving a trust-region sequential quadratic programming (SQP) subproblem at ea...
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An algorithm for smooth nonlinear constrained optimization problems is described, in which a sequence of feasible iterates is generated by solving a trust-region sequential quadratic programming (SQP) subproblem at each iteration and by perturbing the resulting step to retain feasibility of each iterate. By retaining feasibility, the algorithm avoids several complications of other trust-region SQP approaches: the objective function can be used as a merit function, and the SQP subproblems are feasible for all choices of the trust-region radius. Global convergence properties are analyzed under various assumptions on the approximate Hessian. Under additional assumptions, superlinear convergence to points satisfying second-order sufficient conditions is proved.
Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed ...
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Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm ( developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach.
An approach to the coning error evaluation is presented in this paper. Firstly the initial point is calculated based on semi-cone angle and fitting cone axis obtained from fitting circle. Then, this paper puts forward...
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ISBN:
(纸本)9783540885160
An approach to the coning error evaluation is presented in this paper. Firstly the initial point is calculated based on semi-cone angle and fitting cone axis obtained from fitting circle. Then, this paper puts forward a concise and visual model based on adaptive cone and proposes a solution of coning error base on sequential quadratic programming. Finally, the result of calculation based on many examples indicates that the initial point improves the evaluation of coning error.
In this paper, a non-linear multiobjective optimization model is developed to obtain optimal annual scheduling for control of power generation in serial or parallel hydropower plants. Weighted-sum method is used to co...
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ISBN:
(纸本)9781424424948
In this paper, a non-linear multiobjective optimization model is developed to obtain optimal annual scheduling for control of power generation in serial or parallel hydropower plants. Weighted-sum method is used to convert multiobjective optimization to single objective problem. Then, sequential quadratic programming (SQP) is used, based on using Newton's method and Kune-Tucker conditions, to solve optimization problem. Maximization of power generation benefits and minimization of specific water consumption are assumed as objective functions. In this study, stochastic flows from external sources that enter to each reservoir (like rivers and rain) are assumed based on some scenarios for dry, median, and wet years. These various dry, median and wet water inflow scenarios are generated from the gathered fifty-year historical inflow measurements. Besides, turbine power generation is obtained from hill diagrams. The case study refers to hydropower plants network in Karoon River Basin in southern Iran. The corresponding optimization model has 240 variables and 660 constraints and MATLAB software is used to develop the model. Solving such non-linear multiobjective optimization model, annual rule curves that show optimal operation for reservoirs and total yearly energy production are presented for control of the cascaded units.
In this paper the application of Nonlinear Model Predictive Control (NMPC) to a chemical reactor, namely the Open Plate Reactor(OPR), is considered. A control strategy which allows a safe start-up operation of the rea...
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ISBN:
(纸本)9781424422869
In this paper the application of Nonlinear Model Predictive Control (NMPC) to a chemical reactor, namely the Open Plate Reactor(OPR), is considered. A control strategy which allows a safe start-up operation of the reactor and provides an easy handling of system restrictions and nonlinearities, is proposed. An efficient open-loop parameterization of the control signals is used to further reduce the size of the optimization variables. It is shown that the NMPC algorithm developed gives satisfactory operation and the reduced open loop parameterization results in a sufficiently rich closed loop behavior.
Computational fluid dynamics and mathematical optimization were used to investigate the mixing effectiveness of jets in crossflow. A numerical model was developed, validated, and calibrated against experimental measur...
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Computational fluid dynamics and mathematical optimization were used to investigate the mixing effectiveness of jets in crossflow. A numerical model was developed, validated, and calibrated against experimental measurements of a temperature distribution at different cross-sectional planes downstream of an orifice injection plane. Good agreement was obtained when the ratio between momentum and species diffusivities was varied according to the jetto-mainstream momentum flux ratio. Numerical optimization of various double-sided jet configurations followed, using a parametric approach. The results obtained showed that changes in orifice size and spacing at a constant orifice-to-mainstream area ratio and momentum flux ratio have a significant influence on mixing effectiveness. The optimum configuration compared favorably with an empirically defined relationship between orifice spacing and momentum flux ratio. Mathematical optimization was then combined with numerical methods to predict the optimum orifice configuration. The results showed the feasibility of using a gradient-based approximation method to allow, for a given set of parameters, the systematic adjustment of design variables to achieve improvement in performance.
This paper presents a continuum-based design sensitivity analysis and optimization of high-frequency radiation problems using the energy finite element method and energy boundary element method. The noise radiated fro...
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This paper presents a continuum-based design sensitivity analysis and optimization of high-frequency radiation problems using the energy finite element method and energy boundary element method. The noise radiated from the vibrating structure at a high-frequency range is obtained through a sequential procedure. The structural energy finite element method calculates structural energy distribution, which is then used as the boundary condition for the energy boundary element method to calculate the energy density at a far-field observation point. For design sensitivity analysis, the direct differentiation method calculates the sensitivity of the exterior noise through the sensitivity of the structural energy density obtained from the energy finite element method. The adjoint variable method calculates the adjoint load from an acoustic energy boundary element method reanalysis, and the adjoint response is obtained from a structural energy finite element method reanalysis. The sensitivity information is obtained by carrying out numerical integration only on the structural finite element part. The proposed design sensitivity analysis approach has been applied in the design of automotive and naval structures to search for the best material layout to achieve the lowest noise level at high frequency.
In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification thro...
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In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification through unlabeled data is the focus. In this article, we introduce a novel large margin semi-supervised learning methodology, using grouping information from unlabeled data, together with the concept of margins, in a form of regularization controlling the interplay between labeled and unlabeled data. Based on this methodology, we develop two specific machines involving support vector machines and psi-learning, denoted as SSVM and SPSI, through difference convex programming. In addition, we estimate the generalization error using both labeled and unlabeled data, for tuning regularizers. Finally, our theoretical and numerical analyses indicate that the proposed methodology achieves the desired objective of delivering high performance in generalization, particularly against some strong performers.
For current sequentialprogramming (SQP) type algorithms, there exist two problems. One is that there are many quadraticprogramming subproblems are needed to be solved per iteration, the other is that the search dire...
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For current sequentialprogramming (SQP) type algorithms, there exist two problems. One is that there are many quadraticprogramming subproblems are needed to be solved per iteration, the other is that the search direction may be unbounded and caused the sequence to be divergent. In this paper, we presented a modified SQP-filter method based on the modified quadratic subproblem proposed in Zhou [G. L. Zhou, A modified SQP method and its global convergence, J. Global Optim. 11 (1997) 193-205]. This method has no demand on the initial point, and need not using a penalty parameter, which could be problematic to obtain. What is more, the subproblem is feasible at each iterate point. Under some conditions, the global convergence property is obtained. (c) 2007 Elsevier Inc. All rights reserved.
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