scholarly journals Improved Chicken Swarm Optimization Method for Reentry Trajectory Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Wu ◽  
Bo Yan ◽  
Xiangju Qu
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
High Dimensional ◽  
Local Optimum ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization ◽  
Chicken Swarm Optimization ◽  
Dimensional Optimization

Reentry trajectory optimization has been researched as a popular topic because of its wide applications in both military and civilian use. It is a challenging problem owing to its strong nonlinearity in motion equations and constraints. Besides, it is a high-dimensional optimization problem. In this paper, an improved chicken swarm optimization (ICSO) method is proposed considering that the chicken swarm optimization (CSO) method is easy to fall into local optimum when solving high-dimensional optimization problem. Firstly, the model used in this study is described, including its characteristic, the nonlinear constraints, and cost function. Then, by introducing the crossover operator, the principles and the advantages of the ICSO algorithm are explained. Finally, the ICSO method solving the reentry trajectory optimization problem is proposed. The control variables are discretized at a set of Chebyshev collocation points, and the angle of attack is set to fit with the flight velocity to make the optimization efficient. Based on those operations, the process of ICSO method is depicted. Experiments are conducted using five algorithms under different indexes, and the superiority of the proposed ICSO algorithm is demonstrated. Another group of experiments are carried out to investigate the influence of hen percentage on the algorithm’s performance.

Coupled Shape and Reentry Trajectory Optimization of Entry Vehicle for Lunar Return

2012 ◽  
Vol 591-593 ◽  
pp. 2624-2627
Author(s):  
Xu Zhong Wu ◽  
Sheng Jing Tang ◽  
Jie Guo
Keyword(s):  
Trajectory Optimization ◽  
Body Shape ◽  
Optimization Problem ◽  
Population Based ◽  
Impact Theory ◽  
Peak Heat Flux ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization ◽  
Semi Empirical ◽  
Pareto Frontiers

This paper deals with the reentry trajectory optimization problem for lunar return with consideration of entry vehicle’s fore-body shape. Three performance objectives are applied in this work: cross range, peak heat flux and total heat load. Aerothermodynamic models are based on modified Newtonian impact theory and semi-empirical correlations for convective and radiative stagnation-point heat transfer. A population based evolutionary algorithm has been executed to optimize the multidisciplinary problem. At last the numerical example showed the Pareto frontiers for spherical segment and sphere cone respectively, one of optimal trajectory designs selected from the Pareto frontiers are showed in this paper. The mission requirements are satisfied through the aerothermodynamic balance.

A New Simple Micro-PSO for High Dimensional Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1195-1200
Author(s):  
Wen Hua Han
Keyword(s):  
Optimization Problem ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Stochastic Search ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Dimensional Optimization

The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.

Reentry Trajectory Optimization Based on Time-Domain Improved Particle Swarm Optimization

2015 ◽  
Vol 734 ◽  
pp. 482-486
Author(s):  
Bo Yang ◽  
Hai Xiao Wang
Keyword(s):  
Time Domain ◽  
Trajectory Optimization ◽  
Pso Algorithm ◽  
Basis Functions ◽  
Final Time ◽  
Swarm Optimization ◽  
Reentry Vehicle ◽  
Time Optimal ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization

A new time-domain improved PSO algorithm is proposed to solve the problem of reentry trajectory optimization. The approach uses time-domain basis functions fitting the control variables, solves free final time optimal control directly, and sets parameters by using vehicle's dynamic characteristics. Simulation of a reentry vehicle with no-fly zone constraints is used to demonstrate the effectiveness and veracity of algorithm in reentry trajectory optimization. The final condition error is less than 1%.

Reentry Trajectory Optimization and Simulation of Hypersonic Vehicle with Maximum Cross Range Based on GPM

2014 ◽  
Vol 635-637 ◽  
pp. 1431-1437
Author(s):  
Wu Jun Huo ◽  
Xu Liu ◽  
Li Wang ◽  
Chao Song
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
Control Variable ◽  
Pseudospectral Method ◽  
Hypersonic Vehicle ◽  
State Variables ◽  
Hypersonic Aircraft ◽  
Gauss Pseudospectral Method ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization

Abstract：The application of Gauss pseudospectral method (GPM) to hypersonic aircraft reentry trajectory optimization problem with maximum cross range was introduced. The Gauss pseudospectral method was used to solve the reentry trajectory of the hypersonic vehicle with the maximum cross range. Firstly, the model of hypersonic aircraft reentry trajectory optimization control problem was established. Taking no account of course constraint, the maximum cross range was chosen as optimal performance index, and angle of attack and bank was chosen as control variable. Terminal state was constrained by position and velocity. Then GPM was applied to change trajectory optimization problem into nonlinear programming problem (NLP), and the state variables and control variables were selected as optimal parameters at all Gauss nodes. At last, optimal reentry trajectory was solved by solving the NLP with the help of SNOPT. The simulation results indicate that GPM does not need to estimate the initial cost variable, and it is not sensitive to the initial states and effective to solve trajectory optimization problem.

scholarly journals Reentry Trajectory Optimization using Gradient Free Algorithms

2018 ◽  
Vol 51 (1) ◽  
pp. 650-655 ◽  
Author(s):  
G. Naresh Kumar ◽  
Md Shafeeq Ahmed ◽  
A.K. Sarkar ◽  
S.E. Talole
Keyword(s):  
Trajectory Optimization ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization
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