A modified pseudospectral method for solving trajectory optimization problems with singular arc

2016 ◽  
Vol 40 (5) ◽  
pp. 1783-1793 ◽  
Author(s):  
Zahra Foroozandeh ◽  
Mostafa Shamsi ◽  
Vadim Azhmyakov ◽  
Masoud Shafiee
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ningbo Li ◽  
Humin Lei ◽  
Lei Shao ◽  
Tao Liu ◽  
Bin Wang

In order to improve the optimization accuracy and convergence rate for trajectory optimization of the air-to-air missile, a multi-interval mesh refinement Radau pseudospectral method was introduced. This method made the mesh endpoints converge to the practical nonsmooth points and decreased the overall collocation points to improve convergence rate and computational efficiency. The trajectory was divided into four phases according to the working time of engine and handover of midcourse and terminal guidance, and then the optimization model was built. The multi-interval mesh refinement Radau pseudospectral method with different collocation points in each mesh interval was used to solve the trajectory optimization model. Moreover, this method was compared with traditional h method. Simulation results show that this method can decrease the dimensionality of nonlinear programming (NLP) problem and therefore improve the efficiency of pseudospectral methods for solving trajectory optimization problems.


2011 ◽  
Vol 383-390 ◽  
pp. 7375-7380 ◽  
Author(s):  
Bo Yang ◽  
Shang Sun

According to the nonlinear, multivariable and multi-constraint features of the reentry trajectory optimization problem of airbreathing hypersonic vehicles, a suboptimal solution method is developed. The reentry trajectory generation is converted to a nonlinear programming (NLP) problem by using Gauss pseudospectral method (GPM). The state and control variables on Gauss nodes are chosen as parameters to be optimized and the minimum total heat absorption is chosen as the optimal performance index. Then the sequential quadratic programming (SQP) method is used to solve the NLP problem. The states of optimized trajectory are compared with the states obtained by the integral of kinetic equations. By simulating on an example of airbreathing hypersonic vehicles, it is demonstrated that the above method is not sensitive to the estimate of motion states and is easier to converge. And the method is effective to solve trajectory optimization problems.


2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yi Cui ◽  
Xintong Fang ◽  
Gaoqi Liu ◽  
Bin Li

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>


2018 ◽  
Vol 189 ◽  
pp. 10019
Author(s):  
Hao Li ◽  
Changzhu Wei

A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.


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