The simultaneous strategy and control parameterization are the two most widely used direct methods for solving dynamic optimization problems. The control parameterization approach generally results in a comparatively ...
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The simultaneous strategy and control parameterization are the two most widely used direct methods for solving dynamic optimization problems. The control parameterization approach generally results in a comparatively smaller nonlinear program (NLP) but has difficulties in dealing with the path constraints. The advantage of the simultaneous approach lies in its ability to handle path constraints, thus eliminating the need to obtain expensive and possibly infeasible intermediate solutions. The disadvantage is that it requires solution of a potentially very large dimensional NLP. This work presents a decomposition strategy, which combines the advantages of the control parameterization and simultaneous approaches for solving dynamic optimization problems with path constraints. Using the proposed strategy, first the set of state variables x is divided into two sets x(1) and x(2), and the system is partitioned into two corresponding sub-systems. The criterion used to partition the state variables and the system model are that the equations which define the state variables involved in the path constraints should be in the same sub-system. For the resulting sub-systems, one is enforced in the master NLP through collocation method as in simultaneous approach, the other is solved together with the sensitivities in a differential and algebraic equations (DAE) solver. This strategy constitutes a general approach in the sense that for problems with a specific structure the method is equivalent to the control parameterization method, while for other problems with special structures the approach is the same as the simultaneous approach. In general it possesses the advantage of the simultaneous method in handling the path constraints since it is directly enforced in the master NLP as well as the advantage of control parameterization in resulting a small master NLP because only a fraction of the state variables are directly discretized. The method is demonstrated through several num
作者：
Fahroo, FRoss, IMUSN
Postgrad Sch Dept Math Monterey CA 93943 USA USN
Postgrad Sch Dept Aeronaut & Astronaut Monterey CA 93943 USA
We present a Chebyshev pseudospectral method for directly solving a generic Bolza optimal control problem with state and control constraints. This method employs Nth-degree Lagrange polynomial approximations for the s...
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We present a Chebyshev pseudospectral method for directly solving a generic Bolza optimal control problem with state and control constraints. This method employs Nth-degree Lagrange polynomial approximations for the state and control variables with the values of these variables at the Chebyshev-Gauss-Lobatto (CGL) points as the expansion coefficients. This process yields a nonlinear programming problem (NLP) with the state and control values at the CGL points as unknown NLP parameters. Numerical examples demonstrate that this method yields more accurate results than those obtained from the traditional collocation methods.
Four time-optimal, vertical plane, cobralike pitch maneuvers corresponding to different sets of boundary conditions have been studied for the F-18 high-angle-of-attack research vehicle aircraft. These maneuvers are po...
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Four time-optimal, vertical plane, cobralike pitch maneuvers corresponding to different sets of boundary conditions have been studied for the F-18 high-angle-of-attack research vehicle aircraft. These maneuvers are poststall maneuvers that enable an aircraft that is initially followed by a pursuing aircraft to shift positions, that is, the initial evader turns into the pursuer. The initial pursuer is assumed to remain at constant speed and altitude. In addition an 80-deg pitch reversal maneuver has been considered that was optimized using a Chebyshev approach. A nonlinear programming and collocation method was successfully used to find the optimal vertical plane trajectories in open-loop form. All trajectories that were found have been put through a six-degree-of-freedom simulation utilizing a nonlinear inversion closed-loop control technique. The results showed that the optimal vertical plane trajectories can be flown quite accurately, using longitudinal and lateral thrust vectoring, along with the conventional aerodynamic controls.
Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identified as an important task for key process applications. In this study, we present an improved algorithm for si...
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Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identified as an important task for key process applications. In this study, we present an improved algorithm for simultaneous strategies for dynamic optimization. This approach addresses two important issues for dynamic optimization. First, an improved nonlinear programming strategy is developed based on interior point methods. This approach incorporates a novel filter-based line search method as well as preconditioned conjugate gradient method for computing search directions for control variables. This leads to a significant gain in algorithmic performance. On a dynamic optimization case study, we show that nonlinear programs (NLPs) with over 800,000 variables can be solved in less than 67 CPU minutes. Second, we address the problem of moving finite elements through an extension of the interior point strategy. With this strategy we develop a reliable and efficient algorithm to adjust elements to track optimal control profile breakpoints and to ensure accurate state and control profiles. This is demonstrated on a dynamic optimization for two distillation columns. Finally, these algorithmic improvements allow us to consider a broader set of problem formulations that require dynamic optimization methods. These topics and future trends are outlined in the last section. (C) 2002 Published by Elsevier Science Ltd.
作者：
Liu, XUniv Alberta
Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada
The filled function method is an approach to find the global minimum of multimodal and multidimensional functions. This paper proposes a new filled function. This function needs only one parameter and includes neither...
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The filled function method is an approach to find the global minimum of multimodal and multidimensional functions. This paper proposes a new filled function. This function needs only one parameter and includes neither exponential terms nor logarithmic terms. Furthermore, the lower bound of weight factor a is usually smaller than that of one previous formulation. Therefore, it is reasonable to expect that the proposed function has better computability than the previously reported ones. (C) 2002 Elsevier Science Inc. All rights reserved.
A trajectory optimization tool was developed which hybridized a genetic algorithm (GA) with sequential quadratic programming (SQP). When applied to the difficult problem of preliminary mission planning for high-perfor...
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A trajectory optimization tool was developed which hybridized a genetic algorithm (GA) with sequential quadratic programming (SQP). When applied to the difficult problem of preliminary mission planning for high-performance solar sail transfers to displaced non-Keplerian orbits (NKOs), it appeared to identify near-optimal trajectories with relative ease. A small number of segments reduced the computational burden imposed by using a GA.
This note presents an aircraft turbofan engine model identification in the time domain. The identification makes use of a multivariable linear model of turbofan engine dynamics having uncertain eigenvalues. The uncert...
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This note presents an aircraft turbofan engine model identification in the time domain. The identification makes use of a multivariable linear model of turbofan engine dynamics having uncertain eigenvalues. The uncertainty describes the difference between nonlinear and linear models. The bounds for real and imaginary parts of uncertain matrix eigenvalues are determined using nonlinear programming. The results of identification application using a detailed nonlinear model of an aircraft turbofan engine are presented.
作者：
Lee, HSKorea Univ
Dept Architectural Engn Seoul 136701 South Korea
It was shown in the previous study (Lee and Bertero 1993) that incremental collapse can lead to the exhaustion of the plastic rotation capacity at critical regions in a structure when subjected to the number of load c...
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It was shown in the previous study (Lee and Bertero 1993) that incremental collapse can lead to the exhaustion of the plastic rotation capacity at critical regions in a structure when subjected to the number of load cycles and load intensities as expected during maximum credible earthquakes and that this type of collapse can be predicted using the shakedown analysis technique. In this study, a minimum-weight design methodology, which takes into account not only the prevention of this incremental collapse but also the requirements of the serviceability limit states, is proposed by using the shakedown analysis technique and a nonlinear programming algorithm (gradient projection method).
A simple solar sail mass model was used with parametric trajectory data to determine the optimum solar sail payload mass fraction for a Mars cargo mission. It was found that the solar sail should be loaded with a payl...
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A simple solar sail mass model was used with parametric trajectory data to determine the optimum solar sail payload mass fraction for a Mars cargo mission. It was found that the solar sail should be loaded with a payload mass fraction of approximately 70 percent to ensure that the mean rate of payload mass transfer is maximized. Although the analysis assumed simplified trajectories, and, thus, ignored the vagaries of particular mission opportunities, it does indicate an optimum solar sail payload mass fraction that will maximize the mean payload mass delivery rate to Mars. Finally, a similar analysis can also be performed for solar sails utilized for orbit transfer vehicles delivering payload to geosynchronous orbit, for example. (CSA)
Nonnegative color analysis filters are obtained by using an invertible linear transformation of characteristic spectra, which are orthogonal vectors from a principal component analysis (PCA) of a representative ensemb...
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Nonnegative color analysis filters are obtained by using an invertible linear transformation of characteristic spectra, which are orthogonal vectors from a principal component analysis (PCA) of a representative ensemble of color spectra. These filters maintain the optimal compression properties of the PCA scheme. Linearly constrained nonlinear programming is used to find a transformation that minimizes the noise sensitivity of the filter set. The method is illustrated by computing analysis and synthesis filters for an ensemble of measured Munsell color spectra. (C) 2002 Optical Society of America.
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