Optimal control - A review of theory and practice.

Paiewonsky, Bernard

doi:10.2514/3.3307

Cited by 36 publications

(4 citation statements)

References 102 publications

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“…One such approach is Optimal Control, which, as the name would suggest, is a technique that tries to operate at the most optimized conditions, such as a minimum time or minimum fuel burn case. Paiewonsky provides some background into optimal control theory, as well as a variety of examples illustrating application [14].…”

Section: Current Design Methodsmentioning

confidence: 99%

Genetic Fuzzy Controller for a Gas-Turbine Fuel System

Vick¹,

Cohen²

2012

Advances in Intelligent and Autonomous Aerospace Systems

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In this study, a fuel system controller for a gas turbine engine was examined. Controller design in this application is challenging due to nonlinearities in the closed loop system, as well as uncertainties associated with hardware components from part variation or degradation. Current closed loop design methodologies are discussed, as are the limitations or challenges facing these systems. Details on fuzzy logic control and its benefits in this type of application are explored.Information on genetic algorithms is presented, along with a study on how this optimization approach can be utilized to enhance the fuzzy logic controller process. A fuzzy logic controller structure was developed for providing closed loop fuel control in the gas turbine application, using a genetic algorithm to tune the system to provide an accurate and fast response to changing input demands. With a genetic fuzzy controller in place, closed loop analysis was performed, along with a stochastic robustness analysis to assess controller performance in an uncertain environment. Results show that the genetic fuzzy system performed well in this application, resulting in a system with fast rise and settling times to stepping inputs, while also minimizing overshoot and steady state error. Robustness characteristics of the fuzzy controller were also demonstrated, as the stochastic robustness analysis yielded acceptable performance in each simulation of the closed loop system with uncertainties included.ii iii

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Section: Current Design Methodsmentioning

confidence: 99%

Genetic Fuzzy Controller for a Gas-Turbine Fuel System

Vick¹,

Cohen²

2012

Advances in Intelligent and Autonomous Aerospace Systems

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“…where u is a gravitational constant and r is the position u = -so (9) The vector h is the angular momentum of the orbiting body and e is a vector whose magnitude is the orbital eccentricity and whose direction is the direction of the pericenter of the orbit. Except in the singular case h a = 0 value thereafter.…”

Section: Optimal Rocket Steering As a Boundary-value Problemmentioning

confidence: 99%

Rapid Computation of Optimal Trajectories

Brown¹,

Johnson²

1967

IBM J. Res. & Dev.

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A generalized "indirect" method of solving two-point boundary value problems is discussed in applicationto the problem of computing optimal trajectories in a vacuum. Improved numerical techniques make the method extremely fast when a good initial estimateof the solution is available, but it also converges, more slowly, from initial estimates that are far from the solution. Transversality conditions are combined with final-value constraints enablingthe method to solve directly problemsdefined by constraints on arbitrary functions of final state. Section1 defines the differential equationsand initial and terminalconditionsfor optimalrocket trajectories in a centralgravitational field. The differential equationsare given a particularly simple form and transversality conditionsare formulatedanalytically for typical orbital injectionmissions. Section2 defines efficient numerical procedures for solving the initial value problem of optimal trajectories and so reduces the boundary value problemto a multidimensional zero-finding problem. Section 3 describes the generalized version of Newton's method used to solvethis multidimensional zero-finding problem. Section4 summarizes the results of an IBM 7094 implementation, giving execution times and convergence properties.

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“…Even in papers on aeronautical design from the early 1960s, the use of squared slack variables is discussed, with reference lists leading back to this early work. For example, in Paiewonsky (1965Paiewonsky ( , p. 1989 we find the comments (with reference citations omitted):…”

Section: / Stephen G Nashmentioning

confidence: 99%

“…Some early references are given in the quote from Paiewonsky (1965) The situation is summarized in Hestenes (1975, p. vii):…”

Section: Appendix B the Kkt Conditionsmentioning

confidence: 99%

SUMT (Revisited)

Garbe¹,

Glazebrook²,

Nash³

1998

Operations Research

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Fiacco and McCormick's 1968 book is a classic. It arose out of the authors' activities at the Research Analysis Corporation, and as such it is the record of an ambitious and productive collaboration. But it is more than that. The topics they studied and the situations they encountered are representative of the experiences of other researchers in the 1960s. At a deeper level, Fiacco and McCormick's book illustrates the influence of two great traditions of research: one based on linear programming, and the other based on the calculus of variations. The work of Fiacco and McCormick remains influential, as do the influences of these two traditions. An understanding of these influences illuminates not only the history of nonlinear programming, but also its present and future.

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Optimal control - A review of theory and practice.

Cited by 36 publications

References 102 publications

Genetic Fuzzy Controller for a Gas-Turbine Fuel System

Genetic Fuzzy Controller for a Gas-Turbine Fuel System

Rapid Computation of Optimal Trajectories

SUMT (Revisited)

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