时间依赖的西尔维斯特方程和机器人操纵器运动规划的相互性张神经动力学方法
概括
本研究介绍了一种新的无反向互惠型张神经动力学 (RKZN) 模型和一个离散的RKZN (DRKZN) 算法,用于解决机器人运动规划的时间依赖的西尔维斯特方程 (TDSEs). 与传统方法相比,RKZN方法提供了更高的性能和稳定性.
科学领域:
- 控制理论 控制理论
- 机器人技术 机器人技术 机器人技术
- 计算数学 计算数学 计算数学
背景情况:
- 西尔维斯特方程在工业情报控制中至关重要.
- 时间依赖的西尔维斯特方程 (TDSEs) 对于实时机器人操纵器运动规划至关重要.
- 经典的张神经动力学 (ZN) 面临着时间依赖逆矩阵计算的挑战.
研究的目的:
- 开发一种没有逆向的方法来解决TDSEs.
- 用新型神经动力学模型解决机器人操纵器中的运动规划挑战.
- 分析拟议方法的趋同性和稳定性.
主要方法:
- 引入了相互类型的ZN (RKZN) 模型,这是基于能量归零的无反向方法.
- 为未来的西尔维斯特方程 (FSE) 问题提出了一个离散的RKZN (DRKZN) 算法.
- 利用利亚普诺夫稳定理论和非线性系统的比较方法来分析收和稳定性.
主要成果:
- 在没有逆矩阵计算的情况下,RKZN模型有效地解决TDSEs.
- DRKZN算法解决了FSEs和机器人运动规划的挑战.
- 收和稳定性分析证实了RKZN方法的有效性.
结论:
- RKZN方法为TDE和机器人运动规划提供了卓越和强大的解决方案.
- DRKZN算法将应用范围扩展到FSE问题.
- 数字,模拟和物理实验验证拟议方法的有效性.
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