Evolutionary Computation for Discrete and Continuous Time Optimal Control Problems

Crispin, Yechiel

doi:10.1007/978-1-4020-5626-0_8

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Olsen and Fowler [Olsen & Fowler (2005)] also adopted the GA to generate a near optimal solution to a rendezvous problem using elliptic orbits. Crispin [Crispin (2006;2007)] obtained GA based solutions to rendezvous problems as nonlinear discrete or continuous time optimal control problems with terminal constraints. Using the GA has the advantage of completely eliminating the need for a TPBVP reformulation.…”

Section: Introductionmentioning

confidence: 99%

Rendezvous between Two Active Spacecraft with Continuous Low Thrust

Crispin¹,

Seo²

2011

Advances in Spacecraft Technologies

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Section: Introductionmentioning

confidence: 99%

Rendezvous between Two Active Spacecraft with Continuous Low Thrust

Crispin¹,

Seo²

2011

Advances in Spacecraft Technologies

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“…Genetic algorithms (GAs) provide a powerful alternative method for solving optimal control problems. They have been used to solve control problems (Crispin, 2006(Crispin, , 2007, orbital transfer and rendezvous problems (Crispin and Ricour, 2007). GAs use a stochastic search method and are robust when compared to gradient methods.…”

Section: Introductionmentioning

confidence: 99%

Interception and Rendezvous Between Autonomous Vehicles

Crispin¹

2008

Advances in Robotics, Automation and Control

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“…PSO) mimics the social behavior of a swarm of insects, see for example (Venter, 2002), (Crispin,2005). Genetic Algorithms (GAs) (Goldberg, 1989) are a powerful alternative method for solving optimal control problems, see also (Crispin, 2006 and2007). GAs use a stochastic search method and are robust when compared to gradient methods.…”

Section: Introductionmentioning

confidence: 99%

Interception and Cooperative Rendezvous Between Autonomous Vehicles

Crispin¹,

Ricour²

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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The rendezvous problem between autonomous vehicles is formulated as an optimal cooperative control problem with terminal constraints. A major approach to the solution of optimal control problems is to seek solutions which satisfy the first order necessary conditions for an optimum. Such an approach is based on a Hamiltonian formulation, which leads to a difficult two-point boundary-value problem. In this paper, a different approach is used in which the control history is found directly by a genetic algorithm search method. The main advantage of the method is that it does not require the development of a Hamiltonian formulation and consequently, it eliminates the need to deal with an adjoint problem. This method has been applied to the solution of interception and rendezvous problems in an underwater environment, where the direction of the thrust vector is used as the control. The method is first tested on an interception chaser-target problem where the passive target vehicle moves along a straight line at constant speed. We then treat a cooperative rendezvous problem between two active autonomous vehicles. The effects of gravity, thrust and viscous drag are considered and the rendezvous location is treated as a terminal constraint.

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Evolutionary Computation for Discrete and Continuous Time Optimal Control Problems

Cited by 4 publications

References 11 publications

Rendezvous between Two Active Spacecraft with Continuous Low Thrust

Rendezvous between Two Active Spacecraft with Continuous Low Thrust

Interception and Rendezvous Between Autonomous Vehicles

Interception and Cooperative Rendezvous Between Autonomous Vehicles

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