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%.

A Novel Migrant PSO Algorithm for Vehicle Reentry Trajectory Optimization

2012 ◽  
Vol 9 (2) ◽  
pp. 276-285 ◽  
Author(s):  
Fu-Qiang Xie ◽  
Yong-Ji Wang ◽  
Cheng-Yu Hu ◽  
Zong-Zhun Zheng ◽  
Quan-Min Zhu
Keyword(s):  
Trajectory Optimization ◽  
Pso Algorithm ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization

scholarly journals Time-Optimal Trajectory Optimization of Serial Robotic Manipulator With Kinematic and Dynamic Limits Based On Improved Particle Swarm Optimization

2021 ◽  
Author(s):  
Yu Yang ◽  
Hongze Xu ◽  
Shaohua Li ◽  
Lingling Zhang ◽  
Xiuming Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Control ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Movement Time ◽  
Pso Algorithm ◽  
Motion Controller ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Time Optimal

Abstract Effective motion control can achieve accurate and fast positioning and movement of industrial robotics and improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics and can improve motion efficiency by providing high-speed and reasonable motion references to the motion controller. In this study, a new time-optimal TO strategy, polynomial interpolation function (PIF) combined with improved particle swarm optimization (PSO) considering kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator along a randomly assigned path. The 4-3-4 PIF is first used to generate the smooth and 3-order continuous movement trajectories of six joints in joint space. The PSO with cosine decreasing weight (CDW-PSO) algorithm further reduces the trajectories movement time considering the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque. Experimental results show that the CDW-PSO algorithm achieves a better convergence rate of 23 and a better fitness value of 2.46 compared with the PSO with constant weight and linearly decreasing weight algorithms. The CDW-PSO optimized movement time is reduced by 83.6% compared to the manually setting movement time value of 15. The proposed time-optimal TO strategy can be conducted easily and directly search for global optimal solutions without approximation of the limits. The optimized trajectories could be incorporated in the motion controller of the actual manipulators due to considering the kinematic and dynamic limits.

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.

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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