To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root u...To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.展开更多
The global asymptotical stability for a class of stochastic delayed neural networks （SDNNs） with Maxkovian jumping parameters is considered. By applying Lyapunov functional method and Ito＇s differential rule, new d...The global asymptotical stability for a class of stochastic delayed neural networks （SDNNs） with Maxkovian jumping parameters is considered. By applying Lyapunov functional method and Ito＇s differential rule, new delay-dependent stability conditions are derived. All results are expressed in terms of linear matrix inequality （LMI）, and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.展开更多
The robust guaranteed cost filtering problem for a dass of linear uncertain stochastic systems with time delays is investigated. The system under study involves time delays, jumping parameters and Brownian motions. Th...The robust guaranteed cost filtering problem for a dass of linear uncertain stochastic systems with time delays is investigated. The system under study involves time delays, jumping parameters and Brownian motions. The transition of the jumping parameters in systems is governed by a finite-state Markov process. The objective is to design linear memoryless filters such that for all uncertainties, the resulting augmented system is robust stochastically stable independent of delays and satisfies the proposed guaranteed cost performance. Based on stability theory in stochastic differential equations, a sufficient condition on the existence of robust guaranteed cost filters is derived. Robust guaranteed cost filters are designed in terms of linear matrix inequalities. A convex optimization problem with LMI constraints is formulated to design the suboptimal guaranteed cost filters.展开更多
针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅...针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅格修改了剪枝规则与跳点判断规则,再利用蜂窝栅格特点设计了新的启发式函数来提高搜索效率,通过找寻最远节点的节点更新规则来优化生成的轨迹。利用Matlab仿真平台验证算法的搜索效率和安全性,结果表明,相较于传统JPS算法,采用H-JPS算法进行路径规划能够完全消除危险节点,路径规划时间和长度分别缩短了41.9%和11.1%,显著提高了搜索效率。展开更多
针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地...针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地图进行预处理,获得简化跳点;其次,通过简化跳点对栅格地图进行信息素初始化,以加强简化跳点的引导能力和减少前期盲目搜索;接着,设计蚂蚁死亡惩罚机制,以降低陷入死锁蚂蚁走过路径的信息素,减少死锁问题的发生;再者,通过重新设计启发式信息函数并引入分级式信息素因子改进状态转移概率函数,以提高收敛速度,缩短路径长度;最后,采用路径优化策略删减不必要路径节点,以进一步缩短路径长度、提升平滑度,提高路径质量。仿真结果表明,在简单环境中,JPOACO算法求得的路径长度较传统蚁群算法和另一种优化蚁群算法短约22.6%和2.0%,收敛迭代次数、收敛时间分别减少约77.0%、77.5%和49.3%、87.8%,零死亡迭代次数和零死亡时间较后者减少约19.5%和80.5%;在复杂菠萝种植环境中,JPOACO算法较传统蚁群算法和另一种优化蚁群算法求得的路径长度短16.6%和4.7%,收敛迭代次数、收敛时间分别减少约77.1%、17.4%和73.7%、47.4%,零死亡迭代次数和零死亡时间较后者减少约34.3%和58.2%,表明本文算法具有较高的适用性和可行性。展开更多
基金This work was supported by National Natural Science Foundation of China(12372045)Shanghai Aerospace Science and Technology Program(SAST2021-030).
文摘To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(60874114).
文摘The global asymptotical stability for a class of stochastic delayed neural networks （SDNNs） with Maxkovian jumping parameters is considered. By applying Lyapunov functional method and Ito＇s differential rule, new delay-dependent stability conditions are derived. All results are expressed in terms of linear matrix inequality （LMI）, and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.
文摘The robust guaranteed cost filtering problem for a dass of linear uncertain stochastic systems with time delays is investigated. The system under study involves time delays, jumping parameters and Brownian motions. The transition of the jumping parameters in systems is governed by a finite-state Markov process. The objective is to design linear memoryless filters such that for all uncertainties, the resulting augmented system is robust stochastically stable independent of delays and satisfies the proposed guaranteed cost performance. Based on stability theory in stochastic differential equations, a sufficient condition on the existence of robust guaranteed cost filters is derived. Robust guaranteed cost filters are designed in terms of linear matrix inequalities. A convex optimization problem with LMI constraints is formulated to design the suboptimal guaranteed cost filters.
文摘针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅格修改了剪枝规则与跳点判断规则,再利用蜂窝栅格特点设计了新的启发式函数来提高搜索效率,通过找寻最远节点的节点更新规则来优化生成的轨迹。利用Matlab仿真平台验证算法的搜索效率和安全性,结果表明,相较于传统JPS算法,采用H-JPS算法进行路径规划能够完全消除危险节点,路径规划时间和长度分别缩短了41.9%和11.1%,显著提高了搜索效率。
文摘针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地图进行预处理,获得简化跳点;其次,通过简化跳点对栅格地图进行信息素初始化,以加强简化跳点的引导能力和减少前期盲目搜索;接着,设计蚂蚁死亡惩罚机制,以降低陷入死锁蚂蚁走过路径的信息素,减少死锁问题的发生;再者,通过重新设计启发式信息函数并引入分级式信息素因子改进状态转移概率函数,以提高收敛速度,缩短路径长度;最后,采用路径优化策略删减不必要路径节点,以进一步缩短路径长度、提升平滑度,提高路径质量。仿真结果表明,在简单环境中,JPOACO算法求得的路径长度较传统蚁群算法和另一种优化蚁群算法短约22.6%和2.0%,收敛迭代次数、收敛时间分别减少约77.0%、77.5%和49.3%、87.8%,零死亡迭代次数和零死亡时间较后者减少约19.5%和80.5%;在复杂菠萝种植环境中,JPOACO算法较传统蚁群算法和另一种优化蚁群算法求得的路径长度短16.6%和4.7%,收敛迭代次数、收敛时间分别减少约77.1%、17.4%和73.7%、47.4%,零死亡迭代次数和零死亡时间较后者减少约34.3%和58.2%,表明本文算法具有较高的适用性和可行性。
