In this paper, a novel nonlinear programming based control allocation scheme is developed. The performance of this nonlinear control allocation algorithm is compared with that of other control allocation approaches, i...
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In this paper, a novel nonlinear programming based control allocation scheme is developed. The performance of this nonlinear control allocation algorithm is compared with that of other control allocation approaches, including a mixed optimization scheme, a redistributed pseudo-inverse approach, and a direct allocation (geometric) method. The control allocation methods are first compared using open-loop measures such as the ability to attain commanded moments for a prescribed maneuver. The methods are then compared in closed-loop with a dynamic inversion-based control law. Next, the performance of the different algorithms is compared for different reference trajectories under a variety of failure conditions. Finally, we perform some preliminary studies employing "split actuators" that increase available control authority under failure conditions. All studies are conducted on a re-entry vehicle simulation.
The problem addressed here is to determine controls for moving a load along specified trajectories which avoid obstacles. It is possible to use flat outputs to determine inputs when hoist motion is present. However, w...
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An algorithmic framework for numerically approximating multiparametric nonlinear programming (mp-NLP) solutions is given, along with a method that uses mp-NLP for evaluating the adequacy of the nominal model used in I...
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In this paper, an improved genetic algorithm is proposed for nonlinear programming problems with inequality constraints. In this algorithm, mutation operator is given by mimicking the physics of electromagnetism, and ...
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ISBN:
(纸本)0780384032
In this paper, an improved genetic algorithm is proposed for nonlinear programming problems with inequality constraints. In this algorithm, mutation operator is given by mimicking the physics of electromagnetism, and fitness function is given by evaluation function and objective function. To evaluate the efficiency of the algorithm, the algorithm is applied to two test problems, and our results are compared with other methods.
In several applications, underestimation of functions has proven to be a helpful tool for global optimization. In protein-ligand docking problems as well as in protein structure prediction, single convex quadratic und...
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In several applications, underestimation of functions has proven to be a helpful tool for global optimization. In protein-ligand docking problems as well as in protein structure prediction, single convex quadratic underestimators have been used to approximate the location of the global minimum point. While this approach has been successful for basin-shaped functions, it is not suitable for energy functions with more than one distinct local minimum with a large magnitude. Such functions may contain several basin-shaped components and, thus, cannot be underfitted by a single convex underestimator. In this paper, we propose using an underestimator composed of several negative Gaussian functions. Such an underestimator can be computed by solving a nonlinear programming problem, which minimizes the error between the data points and the underestimator in the L-1 norm. Numerical results for simulated and actual docking energy functions are presented.
Multidisciplinary design optimization is an important approach for the conceptual design of space planes because these planes are characterized by disciplines that interact with one another. A multidisciplinary design...
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Multidisciplinary design optimization is an important approach for the conceptual design of space planes because these planes are characterized by disciplines that interact with one another. A multidisciplinary design optimization problem of a single-stage-to-orbit space,plane is formulated and solved in this study. The modeling and optimization of rigid body characteristics, such as trim and stability, are focused on because a single-stage-to-orbit space plane has a tendency for considerable shift of both the aerodynamic center and the center of gravity. Moreover, the design of the air'-breathing engines are integrated with the airframe and its effect on the rigid body characteristics are also modeled in the framework of multidisciplinary design optimization. Using the all-at-once-based multidisciplinary design optimization approach, which incorporates sparse nonlinear programming and metamodeling, the design of the vehicle and its flight trajectory are successfully optimized. Finally, the characteristics of the optimal solution are investigated, especially the relationships among the airframe-engine integration, rigid body characteristics, and payload transportation capability.
This research presents a new constrained optimization approach for solving systems of nonlinear equations. Particular advantages are realized when all of the equations are convex. For example, a global algorithm for f...
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This research presents a new constrained optimization approach for solving systems of nonlinear equations. Particular advantages are realized when all of the equations are convex. For example, a global algorithm for finding the zero of a convex real-valued function of one variable is developed. If the algorithm terminates finitely, then either the algorithm has computed a zero or determined that none exists;if an infinite sequence is generated, either that sequence converges to a zero or again no zero exists. For solving n-dimensional convex equations, the constrained optimization algorithm has the capability of determining that the system of equations has no solution. Global convergence of the algorithm is established under weaker conditions than previously known and, in this case, the algorithm reduces to Newton's method together with a constrained line search at each iteration. It is also shown how this approach has led to a new algorithm for solving the linear complementarity problem. (c) 2005 Elsevier B.V. All rights reserved.
A new approach for parametric modeling of finite element model for shaping optimization is developed. This approach only needs the finite element mesh, yet can still rebuild a reverse parametric CAD model for efficien...
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A new approach for parametric modeling of finite element model for shaping optimization is developed. This approach only needs the finite element mesh, yet can still rebuild a reverse parametric CAD model for efficient modeling. Special care was paid to make sure the modeling parameters were reduced, but still had enough flexibility for shaping definition. With similar algorithms of the Natural Shape Function, this approach is able to keep the original mesh pattern through out the design iterations, therefore eliminates the response noise produced by re-meshing.. Unlike other methodologies, the current one only needs to define the parameters on the contour of the model. Cutting the whole domain into several subdomains is not necessary. Therefore it is called the Contour Natural Shape Function.
The aim of this paper is to present a novel, transparent approach to a well-established field: the deep methods and applications of the complete analysis of continuous optimization problems. Standard descents give a u...
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The aim of this paper is to present a novel, transparent approach to a well-established field: the deep methods and applications of the complete analysis of continuous optimization problems. Standard descents give a unified approach to all standard necessary conditions, including the Lagrange multiplier rule, the Karush-Kuhn-Tucker conditions and the second order conditions. Nonstandard descents lead to new necessary conditions. These can be used to give surprising proofs of deep central results of fields that are generally viewed as distinct from optimization: the fundamental theorem of algebra, the maximum and the minimum principle of complex function theory, the separation theorems for convex sets, the orthogonal diagonalization of symmetric matrices and the implicit function theorem. These optimization proofs compare favorably with the usual proofs and are all based on the same strategy. This paper is addressed to all practitioners of optimization methods from many fields who are interested in fully understanding the foundations of these methods and of the central results above. (c) 2006 Elsevier B.V. All rights reserved.
The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is propo...
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The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). The error of the MEKF algorithm is proved to be exponentially bounded. Batch and iterated versions of the algorithm are given, too. As an application, the algorithm is used to optimize the parameters in certain nonlinear input-output mappings. Simulation results on interpolation of real data and prediction of chaotic time series are shown.
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