Sequential conjugate-gradient-restoration algorithm for optimal control problems. Part 1. Theory |
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Authors: | J C Heideman A V Levy |
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Institution: | (1) Present address: Exxon Production Research Company, Houston, Texas;(2) Department of Mechanical and Aerospace Engineering and Materials Science, Rice University, Houston, Texas;(3) Present address: Universidad Nacional Autonoma de Mexico, CIMAS, Mexico City, Mexico |
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Abstract: | This paper considers the problem of minimizing a functionalI which depends on the statex(t), the controlu(t), and the parameter π. Here,I is a scalar,x ann-vector,u anm-vector, and π ap-vector. At the initial point, the state is prescribed. At the final point, the state and the parameter are required to satisfyq scalar relations. Along the interval of integration, the state, the control, and the parameter are required to satisfyn scalar differential equations.
First, the case of a quadratic functional subject to linear constraints is considered, and a conjugate-gradient algorithm
is derived. Nominal functionsx(t),u(t), π satisfying all the differential equations and boundary conditions are assumed. Variations Δx(t), δu(t), Δπ are determined so that the value of the functional is decreased. These variations are obtained by minimizing the first-order
change of the functional subject to the differential equations, the boundary conditions, and a quadratic constraint on the
variations of the control and the parameter.
Next, the more general case of a nonquadratic functional subject to nonlinear constraints is considered. The algorithm derived
for the linear-quadratic case is employed with one modification: a restoration phase is inserted between any two successive
conjugate-gradient phases.
In the restoration phase, variations Δx(t), Δu(t), Δπ are determined by requiring the least-square change of the control and the parameter subject to the linearized differential
equations and the linearized boundary conditions. Thus, a sequential conjugate-gradient-restoration algorithm is constructed
in such a way that the differential equations and the boundary conditions are satisfied at the end of each complete conjugate-gradient-restoration
cycle.
Several numerical examples illustrating the theory of this paper are given in Part 2 (see Ref. 1). These examples demonstrate
the feasibility as well as the rapidity of convergence of the technique developed in this paper.
This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant
No. AF-AFOSR-72-2185. The authors are indebted to Professor A. Miele for stimulating discussions.
Formerly, Graduate Studient in Aero-Astronautics, Department of Mechanical and Aerospace Engineering and Materials Science,
Rice University, Houston, Texas. |
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Keywords: | Optimal control gradient methods conjugate-gradient methods numerical methods computing methods gradient-restoration algorithms |
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