Probabilistic guidance of distributed systems using sequential convex programming
Probabilistic guidance of distributed systems using sequential convex programming
复制标题
使用顺序凸规划的分布式系统的概率指导
DOI：
10.1109/iros.2014.6943103
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
F. Hadaegh
中科院分区：
文献类型：
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作者：
D. Morgan;G. P. Subramanian;Saptarshi Bandyopadhyay;Soon;F. Hadaegh
本文利用非齐次马尔可夫链分布式系统的概率制导和序列凸规划的模型预测控制，对大量智能体的编队飞行算法进行了集成、实现和验证。使用非齐次马尔可夫链，当分布式系统收敛到期望的队形时，每个代理在每次迭代期间以统计独立的方式确定其目标位置。此外，分布式系统对外部干扰或对地层的损害具有很强的稳健性。一旦分配了目标位置，就建立了一个最优控制问题，以确保智能体在避免碰撞的同时到达目标位置。该问题使用序列凸规划来确定最优的、无碰撞的轨迹，并实施模型预测控制来在新的状态信息可用时更新这些轨迹。最后，利用编队飞行试验台对分布式系统的概率制导和模型预测控制算法进行了验证。
In this paper, we integrate, implement, and validate formation flying algorithms for a large number of agents using probabilistic guidance of distributed systems with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each iteration in a statistically independent manner while the distributed system converges to the desired formation. Moreover, the distributed system is robust to external disturbances or damages to the formation. Once the target positions are assigned, an optimal control problem is formulated to ensure that the agents reach the target positions while avoiding collisions. This problem is solved using sequential convex programming to determine optimal, collision-free trajectories and model predictive control is implemented to update these trajectories as new state information becomes available. Finally, we validate the probabilistic guidance of distributed systems and model predictive control algorithms using the formation flying testbed.