Trajectory Optimization via Modified Genetic Algorithm

Yokoyama, Nobuhiro; Suzuki, Shinji

doi:10.2514/6.2003-5493

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…The PSO [76][77][78][79][80][81][82], GA [83][84][85][86][87][88][89][90][91], and ant colony optimization (ACO) [92] are the typical stochastic optimal approaches modeled on the concept of evolutionary approach [93]. These intelligent methods are becoming more popular due to their accuracy and speed qualities, which can be used in constrained reentry trajectory optimization of hypersonic vehicles.…”

Section: Intelligent Methodsmentioning

confidence: 99%

“…He made use of the GA to calculate the heat flux along the reentry flight. Yokoyama et al [15,87] then proposed a new selection method using a fixed-parameter penalty function, in which the selection of surviving individuals is carried out on multiple criteria. The resulting reentry trajectory approached the near-optimal solution.…”

Section: Fig 8 Basic Process Of Gamentioning

confidence: 99%

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Progress in reentry trajectory planning for hypersonic vehicle

Zhao

Zhou

Jin

2014

J. of Syst. Eng. Electron.

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The reentry trajectory planning for hypersonic vehicles is critical and challenging in the presence of numerous nonlinear equations of motion and path constraints, as well as guaranteed satisfaction of accuracy in meeting all the specified boundary conditions. In the last ten years, many researchers have investigated various strategies to generate a feasible or optimal constrained reentry trajectory for hypersonic vehicles. This paper briefly reviews the new research efforts to promote the capability of reentry trajectory planning. The progress of the onboard reentry trajectory planning, reentry trajectory optimization, and landing footprint is summarized. The main challenges of reentry trajectory planning for hypersonic vehicles are analyzed, focusing on the rapid reentry trajectory optimization, complex geographic constraints, and cooperative strategies.

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Section: Intelligent Methodsmentioning

confidence: 99%

Section: Fig 8 Basic Process Of Gamentioning

confidence: 99%

Progress in reentry trajectory planning for hypersonic vehicle

Zhao

Zhou

Jin

2014

J. of Syst. Eng. Electron.

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“…A more recently derived rule of thumb is that for "small" parameter sets, the population size is effective if scaled with the number of parameters with 10 m, and for larger spaces the population size scales with ln m ðÞ [42], where the definition of large is different for each author. For a simple parameter set GA can be quite effective for optimization problems in many space based applications [23,24,28,[43][44][45][46][47] which stem from aeronautical control [20,22], and ground-based robotic systems [48][49][50].…”

Section: Genetic Algorithmmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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“…Reference trajectory-based reentry guidance is one of the most widely used methods when dealing with practical engineering problems [2,3]. A lot of researchers did in-depth research on this method and it has been proved to be effective through hundreds of applications [4][5][6][7][8][9]. The first step for generating a reference trajectory is to calculate the heating rate, dynamic pressure, aerodynamic load as well as other constraints of reentry area and to form an impassable boundary in flight profile, which is called the reentry corridor [10,11].…”

Section: Introductionmentioning

confidence: 99%

Improved reference trajectory generation method in reentry guidance

Xiong

Chen

et al. 2014

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference

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After generating reference trajectory in the process of reentry guidance for reusable launch vehicle, if the designed bank angle curve is too close to the constraint boundary, there is a great possibility for the obtained reference trajectory to exceed the reentry corridor boundary. This paper proposed a margin searching method, to reduce the likelihood of generating a reference trajectory which exceeds the reentry corridor boundary and to improve the design success rate.

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Trajectory Optimization via Modified Genetic Algorithm

Cited by 12 publications

References 11 publications

Progress in reentry trajectory planning for hypersonic vehicle

Progress in reentry trajectory planning for hypersonic vehicle

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Improved reference trajectory generation method in reentry guidance

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