.png){kind=icon}
返回
A computationally efficient sequential convex programming using Chebyshev collocation method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ast.2023.108584.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper aims to develop a computationally efficient sequential convex programming algorithm for a class of nonlinear systems. A Chebyshev collocation discretization (CCD) technique is proposed that transforms the continuous convex optimization problem into a finite-dimensional discrete optimization problem. The CCD technique uses Chebyshev polynomials to construct a closed-form approximate solution to the convex dynamic equation. Moreover, it establishes a linear mapping between the control inputs and the system states. This enables the constraints of the dynamics equations to be externalized from the optimization problem and computed using efficient linear equation solving algorithms. On the one hand, this strategy significantly reduces the number of constraint equations and optimization variables, improving the speed of the optimizer. On the other hand, it preserves the path constraints on the system states in the optimization problem. A numerical simulation example of the perching maneuver for a fixed-wing unmanned aerial vehicle is presented to validate the efficiency of the algorithm. The result shows significant improvements in both speed and accuracy compared to the Euler discretization, and greatly improves the solution speed while maintaining almost the same solution accuracy compared to the Gauss pseudospectral optimization method.& COPY; 2023 Elsevier Masson SAS. All rights reserved.
Keyword:
Chebyshev collocation
Sequential convex programming
Nonlinear systems
Unmanned aerial vehicle
期刊
IF:
5.8
论文数:
1.0W
被引数:
3.0W
机构
引用论文
Improved sequential convex programming using modified Chebyshev-Picard iteration for ascent trajectory optimization改进的chebyshev-picard迭代序列凸规划在上升轨迹优化中的应用
PREAI
分享收藏Simplified prescribed performance tracking control of uncertain nonlinear systems不确定非线性系统的简化规定性能跟踪控制
PREAI
分享收藏Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently轨迹生成的凸优化: 可靠有效地生成动态可行轨迹的教程
PREAI
分享收藏An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation.png)
SIAM REVIEW
OAAI
SIAM REVIEWIF6.1
OAAI分享收藏
查看更多内容