The Conjugate Gradient Method for Optimal Control Problems PDF Download
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Author:
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Category :
Languages : en
Pages : 7
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Author:
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ISBN:
Category :
Languages : en
Pages : 7
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Author: Henry Ekah-Kunde
Publisher: GRIN Verlag
ISBN: 3668494150
Category : Mathematics
Languages : en
Pages : 29
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Seminar paper from the year 2015 in the subject Mathematics - Applied Mathematics, grade: A, , language: English, abstract: In this research, a novel method to approximate the solution of optimal control problems governed by Volterra integral equations of weakly singular types is proposed. The method introduced here is the conjugate gradient method with a discretization of the problem based on the collocation approach on graded mesh points for non linear Volterra integral equations with singular kernels. Necessary and sufficient optimality conditions for optimal control problems are also discussed. Some examples are presented to demonstrate the efficiency of the method.
Author: John Kendall Willoughby
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 128
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The method of conjugate gradients (CG) has been shown to be a rapidly converging and efficient means of solving unconstrained optimal control problems. This dissertation presents some theoretical and computational characteristics of three modifications to the CG algorithm which make it applicable to control problems with terminal state variable constraints. The penalty function method and the projection method have been used to adapt ordinary gradient methods to constrained problems. It is concluded here that the penalty function technique is no more or less advantageous with the CG method than with other gradient techniques. The projection method is shown to be theoretically less compatible with the CG algorithm than with other gradient methods. However, a stepsize adjustment policy is suggested that preserves the rapid convergence that is characteristic of the CG method. It is also shown that nonlinear instead of linear terminal constraints cause no additional theoretical of computational difficulty. A third adaptation of the CG method is given which is original to this study. The method, called the modified conjugate gradient method (MCG), is applied to constrained problems by using constant Lagrange multipliers which converge to their optimal values as the iteration proceeds. A unique feature of the MCG method is that each control iterate produced by the method causes the constraints to be satisfied exactly. Furthermore, the technique is equally applicable to nonlinear and linear terminal state constraints. (Author).
Author: I.H. Mufti
Publisher: Springer Science & Business Media
ISBN: 3642859607
Category : Mathematics
Languages : en
Pages : 54
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The purpose of this modest report is to present in a simplified manner some of the computational methods that have been developed in the last ten years for the solution of optimal control problems. Only those methods that are based on the minimum (maximum) principle of Pontriagin are discussed here. The autline of the report is as follows: In the first two sections a control problem of Bolza is formulated and the necessary conditions in the form of the minimum principle are given. The method of steepest descent and a conjugate gradient-method are dis cussed in Section 3. In the remaining sections, the successive sweep method, the Newton-Raphson method and the generalized Newton-Raphson method (also called quasilinearization method) ar~ presented from a unified approach which is based on the application of Newton Raphson approximation to the necessary conditions of optimality. The second-variation method and other shooting methods based on minimizing an error function are also considered. TABLE OF CONTENTS 1. 0 INTRODUCTION 1 2. 0 NECESSARY CONDITIONS FOR OPTIMALITY •••••••• 2 3. 0 THE GRADIENT METHOD 4 3. 1 Min H Method and Conjugate Gradient Method •. •••••••••. . . . ••••••. ••••••••. • 8 3. 2 Boundary Constraints •••••••••••. ••••. • 9 3. 3 Problems with Control Constraints ••. •• 15 4. 0 SUCCESSIVE SWEEP METHOD •••••••••••••••••••• 18 4. 1 Final Time Given Implicitly ••••. •••••• 22 5. 0 SECOND-VARIATION METHOD •••••••••••••••••••• 23 6. 0 SHOOTING METHODS ••••••••••••••••••••••••••• 27 6. 1 Newton-Raphson Method ••••••••••••••••• 27 6.
Author: Michal Krizek
Publisher: Springer Science & Business Media
ISBN: 3642185606
Category : Science
Languages : en
Pages : 405
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The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well. Via their combinations practitioners have been able to solve complicated, direct and inverse, multidemensional problems modeled by ordinary or partial differential equations and inequalities, not necessarily linear, optimal control and optimal design being part of these problems. The aim of this book is to present both methods in the context of complicated problems modeled by linear and nonlinear partial differential equations, to provide an in-depth discussion on their implementation aspects. The authors show that conjugate gradient methods and finite element methods apply to the solution of real-life problems. They address graduate students as well as experts in scientific computing.
Author: Olaosebikan Temitayo Emmanuel
Publisher: GRIN Verlag
ISBN: 3346060489
Category : Mathematics
Languages : en
Pages : 184
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Doctoral Thesis / Dissertation from the year 2019 in the subject Mathematics - Applied Mathematics, grade: 96.50, , course: Mathematics, language: English, abstract: In this research, Euler-Lagrange Method approach, for solving optimal control problems of both one dimensional and generalized form was considered. In years past, calculus of variation, has been used to solve functional optimization problems. However, with some special features in Calculus of Variation technique, making it unique in solving functional unconstrained optimization problems, these features will be advantageous to solving optimal control problems if it can be amended and modified in one way or the other. This call for the Euler-Lagrange Method which is a modification of the Calculus of Variation Method for solving optimal control problems. It is desired that, with the construction of the new algorithm, it will circumvent the difficulties undergone in constructing control operators which are embedded in Conjugate Gradient Method (CGM) for solving optimal control problems. Its application on some test problems have shown improvement in the results compared with existing results of solving this class of problems. The objective function values for problems 3, 4, 6, 7, 8, 9 and 10 which are: 1.359141, -5.000, 0.36950416, 0.51699120, 0.27576806, 1.5934159×[10]^(-2) and -3.880763×[10]^(-2) appreciate to the existing results 1.359141, -5.000, 0.4146562, 0.613969, 0.2739811, 1.5935×[10]^(-3) and -3.9992×[10]^(-2) respectively while the objective function values for problems 1, 2 and 5 do not fully appreciate to the existing results with slight differences. These results is an indication that the method has some advantages over some existing computational techniques built to take care of the said problems.
Author: H. N. Koivo
Publisher:
ISBN: 9789517202305
Category :
Languages : en
Pages :
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Author: A. V. Levy
Publisher:
ISBN:
Category :
Languages : en
Pages : 74
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Author: Sie Long Kek
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 0
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In this chapter, an efficient computation approach is proposed for solving a general class of discrete-time optimal control problems. In our approach, a simplified optimal control model, which is adding the adjusted parameters into the model used, is solved iteratively. In this way, the differences between the real plant and the model used are calculated, in turn, to update the optimal solution of the model used. During the computation procedure, the equivalent optimization problem is formulated, where the conjugate gradient algorithm is applied in solving the optimization problem. On this basis, the optimal solution of the modified model-based optimal control problem is obtained repeatedly. Once the convergence is achieved, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, both linear and nonlinear examples are demonstrated to show the performance of the approach proposed. In conclusion, the efficiency of the approach proposed is highly presented.
Author: J. C. Heideman
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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