提升无人机的自主控制能力可有效提高无人机在复杂对抗环境下的作战性能。系统工程是指导复杂系统研制的有效手段,采用系统的思维方法,关注系统的整体效能优化实现。系统工程覆盖复杂工程型号生命周期的全流程活动,包括概念论证、工程...提升无人机的自主控制能力可有效提高无人机在复杂对抗环境下的作战性能。系统工程是指导复杂系统研制的有效手段,采用系统的思维方法,关注系统的整体效能优化实现。系统工程覆盖复杂工程型号生命周期的全流程活动,包括概念论证、工程开发、生产制造、使用服役、综合保障以及系统退出等过程,能够为系统开发中各工程技术的应用,跨专业工程合作以及项目管理建立切实的技术路径。无人机外部应用背景环境与内部体系结构相互交联,是一个典型的“系统之系统”(System of Systems)。无人机自主控制系统是跨域、跨平台的复杂“系统之系统”。本文从系统复杂性出发,介绍了无人机系统组成、自主控制系统以及无人机自主能力等级,以期为无人机自主控制技术的发展提供参考和借鉴。展开更多
To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt ...To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.展开更多
Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve t...Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.展开更多
文摘提升无人机的自主控制能力可有效提高无人机在复杂对抗环境下的作战性能。系统工程是指导复杂系统研制的有效手段,采用系统的思维方法,关注系统的整体效能优化实现。系统工程覆盖复杂工程型号生命周期的全流程活动,包括概念论证、工程开发、生产制造、使用服役、综合保障以及系统退出等过程,能够为系统开发中各工程技术的应用,跨专业工程合作以及项目管理建立切实的技术路径。无人机外部应用背景环境与内部体系结构相互交联,是一个典型的“系统之系统”(System of Systems)。无人机自主控制系统是跨域、跨平台的复杂“系统之系统”。本文从系统复杂性出发,介绍了无人机系统组成、自主控制系统以及无人机自主能力等级,以期为无人机自主控制技术的发展提供参考和借鉴。
文摘To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.
基金Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
文摘Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
