A Novel Cellular Method for the Solution of Nonlinear Stochastic Optimal Control Problems

求解非线性随机最优控制问题的新元胞方法

• 批准号: 9713338
• 负责人: Jian-Qiao Sun
• 金额: $ 15万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9713338&HistoricalAwards=false
• 关键词: Novel; Cellular; Method; Solution; Nonlinear

This research project deals with a new method, called the short-time Gaussian cell mapping method for solving nonlinear stochastic optimal control problems. Stochastic nonlinear optimal control problems can be found in civil engineering structures subject to earthquake, ocean waves, and wind loading excitations, in precision machines, manufacturing processes and robots, and in armed vehicles (land or air), target tracking systems and guidance systems operated in combat or rough environments. Few effective methods exist for obtaining optimal control solutions of complex nonlinear systems to random excitations. The method has been applied to some very challenging nonlinear oscillators under Gaussian white noise excitations and has been proven to be effective and accurate in analyzing complex nonlinear stochastic systems. This project develops this method further for nonlinear stochastic optimal control problems. The results of this research provide the capability to more efficiently and accurately predict and control the response of nonlinear systems subject to stochastic excitations, to better assess the reliability of the system under harsh random environments, and to develop new strategies for controlling the state-constrained stochastic system.

本研究计画针对非线性随机最优控制问题，提出一种新的求解方法，称为短时高斯胞映射法。 随机非线性最优控制问题可以发现在土木工程结构受到地震，海浪，风荷载激励，在精密机械，制造过程和机器人，并在武装车辆（陆地或空中），目标跟踪系统和制导系统在战斗或粗糙的环境中运行。 对于复杂的非线性系统，在随机激励作用下，获得最优控制解的有效方法很少。该方法已应用于高斯白色噪声激励下一些非常具有挑战性的非线性振子，并被证明是分析复杂非线性随机系统的有效和准确的方法。本计画进一步发展此方法于非线性随机最优控制问题。本研究的结果提供的能力，更有效地和准确地预测和控制非线性系统的随机激励的响应，以更好地评估系统的可靠性在恶劣的随机环境下，并开发新的策略，用于控制状态约束随机系统。