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Guzmán José J. - Optimal Control with Aerospace Applications

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Guzmán José J. Optimal Control with Aerospace Applications

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Want to know not just what makes rockets go up but how to do it optimally? Optimal control theory has become such an important field in aerospace engineering that no graduate student or practicing engineer can afford to be without a working knowledge of it. This is the first book that begins from scratch to teach the reader the basic principles of the calculus of variations, develop the necessary conditions step-by-step, and introduce the elementary computational techniques of optimal control. This book, with problems and an online solution manual, provides the graduate-level reader with enough introductory knowledge so that he or she can not only read the literature and study the next level textbook but can also apply the theory to find optimal solutions in practice. No more is needed than the usual background of an undergraduate engineering, science, or mathematics program: namely calculus, differential equations, and numerical integration. Although finding optimal solutions for these problems is a complex process involving the calculus of variations, the authors carefully lay out step-by-step the most important theorems and concepts. Numerous examples are worked to demonstrate how to apply the theories to everything from classical problems (e.g., crossing a river in minimum time) to engineering problems (e.g., minimum-fuel launch of a satellite). Throughout the book use is made of the time-optimal launch of a satellite into orbit as an important case study with detailed analysis of two examples: launch from the Moon and launch from Earth. For launching into the field of optimal solutions, look no further! .;Parameter Optimization -- Optimal Control Theory -- The Euler-Lagrange Theorem -- Application of the Euler-Lagrange Theorem -- The Weierstrass Condition -- The Minimum Principle -- Some Applications -- Weierstrass-Erdmann Corner Conditions -- Bounded Control Problems -- General Theory of Optimal Rocket Trajectories.

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James M Longuski , Jos J. Guzmn and John E. Prussing Space Technology Library Optimal Control with Aerospace Applications 2014 10.1007/978-1-4614-8945-0_1
Springer Science + Business Media New York 2014
1. Parameter Optimization
James M. Longuski 1, Jos J. Guzmn 2 and John E. Prussing 3
(1)
Purdue University, Lafayette, IN, USA
(2)
Orbital Sciences Corporation, Chantilly, VA, USA
(3)
University of Illinois at Urbana-Champaign, Urbana, IL, USA
Abstract
Two major branches of optimization are: parameter optimization and optimal control theory. In parameter optimization (a problem of finite dimensions, that is, where the parameters are not functions of time) we minimize a function of a finite number of parameters.
1.1 Introduction
Two major branches of optimization are: parameter optimization and optimal control theory. In parameter optimization (a problem of finite dimensions, that is, where the parameters are not functions of time) we minimize a function of a finite number of parameters. We only provide an overview of parameter optimization in this chapter, since the main topic of this book is optimal control theory. Optimal control (a problem of infinite dimensions where the parameters are functions of time) seeks Picture 1 , an n-vector, that minimizes something called a functional, which will be defined in Chap.
Parameter optimization, the theory of ordinary maxima and minima, is based on calculus. In general, the (unconstrained) problem could be stated as:
Find:
Picture 2
to minimize:
11 where J is the scalar cost function or index of performance and is a - photo 3
(1.1)
where J is the scalar cost function or index of performance and is a constant n-vector Figure 11 The minimum of a function where x is a - photo 4 is a constant n-vector.
Figure 11 The minimum of a function where x is a 2-vector If the x i are - photo 5
Figure 1.1
The minimum of a function where x is a 2-vector.
If the x i are independent and all the partial derivatives of f are continuous, then a stationary solution, is determined by 12 Equation is a necessary condition for an - photo 6 , is determined by
12 Equation is a necessary condition for an extremum a maximum or a - photo 7
(1.2)
Equation () is a necessary condition for an extremum (a maximum or a minimum). We note that f can be maximized by minimizing f .
The stationary point Picture 8 is a local minimum if the matrix formed by the components, Picture 9 (evaluated at Picture 10 ), is a positive-definite matrix, which provides a sufficient condition for a local minimum. To ensure that the matrix is well defined, all the second partials of f must be continuous.
In Fig. we illustrate an example where Optimal Control with Aerospace Applications - image 11 is a 2-vector. In this case, for Optimal Control with Aerospace Applications - image 12 , the necessary and sufficient conditions are:
Optimal Control with Aerospace Applications - image 13
(1.3a)
Optimal Control with Aerospace Applications - image 14
(1.3b)
and
14 where x 1 and x 2 are infinitesimal arbitrary displacements variations - photo 15
(1.4)
where x 1 and x 2 are infinitesimal arbitrary displacements (variations) from Picture 16 and Optimal Control with Aerospace Applications - image 17 . The inequality must hold for all x 1, x 2 0 (then by definition the matrix is positive definite ). For the 2 2 matrix, Eq.() is satisfied if both of the leading principal minors are positive:
Optimal Control with Aerospace Applications - image 18
(1.5a)
15b where the left-hand side of Eq We note that we can write the - photo 19
(1.5b)
where the left-hand side of Eq.().
We note that we can write the variation:
16 for an infinitesimal arbitrary displacement and conclude that at a - photo 20
(1.6)
for an infinitesimal, arbitrary displacement Picture 21 and conclude that at a stationary point (i.e., Eq.()): J =0. In the general case when Picture 22 is an n -vector, the matrix (formed by Picture 23 ) is positive definite if all n of the leading principal minors are positive.
1.2 Parameter Optimization with Constraints
To include constraints in the optimization problem, it is necessary to describe them algebraically. For example, to describe the condition that a point Optimal Control with Aerospace Applications - image 24 is constrained to lie on a circle of radius R and centered at the origin, we write
Optimal Control with Aerospace Applications - image 25
(1.7)
This statement can be generalized as follows. The n variables Optimal Control with Aerospace Applications - image 26 may be subject to certain relations, called constraints, of the form:
18 with m lt n so there are n m independent variables Of course if m n - photo 27
(1.8)
with m < n so there are n m independent variables. Of course if m = n then the problem is so constrained that there is only one possible solution (or there may be no solution) and we have no optimization problem.
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