The huge and rapid progress in electric drives offers new opportunities to improve the performances of aircraft at all levels：fuel burn,environmental footprint,safety,integration and production,serviceability,and mai...The huge and rapid progress in electric drives offers new opportunities to improve the performances of aircraft at all levels：fuel burn,environmental footprint,safety,integration and production,serviceability,and maintainability.Actuation for safety-critical applications like flight-controls,landing gears,and even engines is one of the major consumers of non-propulsive power.Conventional actuation with centralized hydraulic power generation and distribution and control of power by throttling has been well established for decades,but offers a limited potential of evolution.In this context,electric drives become more and more attractive to remove the natural drawbacks of conventional actuation and to offer new opportunities for improving performance.This paper takes the stock,at both the signal and power levels,of the evolution of actuation for safety-critical applications in aerospace.It focuses on the recent advances and the remaining challenges to be taken toward full electrical actuation for commercial and military aircraft,helicopters,and launchers.It logically starts by emphasizing the specificity of safety-critical actuation for aerospace.The following section addresses in details the evolution of aerospace actuation from mechanically-signaled and hydraulically-supplied to all electric,with special emphasis on research and development programs and on solutions entered into service.Finally,the last section reviews the challenges to be taken to generalize the use of all-electric actuators for future aircraft programs.展开更多
针对汽车电动助力制动系统(Electro-booster,EBooster)的液压力控制中液压负载的非线性和不一致性问题,提出一种基于径向基函数(Radial based function,RBF)神经网络的滑模变结构控制方法。设计EBooster系统压力控制架构,建立液压制动...针对汽车电动助力制动系统(Electro-booster,EBooster)的液压力控制中液压负载的非线性和不一致性问题,提出一种基于径向基函数(Radial based function,RBF)神经网络的滑模变结构控制方法。设计EBooster系统压力控制架构,建立液压制动系统等效结构简化模型,据此设计基于RBF网络滑模变结构的液压力控制方法,通过设计RBF网络的自适应律来实现系统滑模控制参数的自适应调整,并利用李雅普诺夫函数方法分析算法的稳定性。最后搭建电动助力制动系统的快速原型试验平台来验证算法的有效性。试验结果表明,采用RBF神经网络滑模变结构的控制策略对电动助力制动系统液压力的控制误差在2%以内,具有良好的控制效果。研究成果为EBooster系统的压力控制提出一种具有良好自适应性的算法设计思路。展开更多
文摘The huge and rapid progress in electric drives offers new opportunities to improve the performances of aircraft at all levels：fuel burn,environmental footprint,safety,integration and production,serviceability,and maintainability.Actuation for safety-critical applications like flight-controls,landing gears,and even engines is one of the major consumers of non-propulsive power.Conventional actuation with centralized hydraulic power generation and distribution and control of power by throttling has been well established for decades,but offers a limited potential of evolution.In this context,electric drives become more and more attractive to remove the natural drawbacks of conventional actuation and to offer new opportunities for improving performance.This paper takes the stock,at both the signal and power levels,of the evolution of actuation for safety-critical applications in aerospace.It focuses on the recent advances and the remaining challenges to be taken toward full electrical actuation for commercial and military aircraft,helicopters,and launchers.It logically starts by emphasizing the specificity of safety-critical actuation for aerospace.The following section addresses in details the evolution of aerospace actuation from mechanically-signaled and hydraulically-supplied to all electric,with special emphasis on research and development programs and on solutions entered into service.Finally,the last section reviews the challenges to be taken to generalize the use of all-electric actuators for future aircraft programs.
文摘针对汽车电动助力制动系统(Electro-booster,EBooster)的液压力控制中液压负载的非线性和不一致性问题,提出一种基于径向基函数(Radial based function,RBF)神经网络的滑模变结构控制方法。设计EBooster系统压力控制架构,建立液压制动系统等效结构简化模型,据此设计基于RBF网络滑模变结构的液压力控制方法,通过设计RBF网络的自适应律来实现系统滑模控制参数的自适应调整,并利用李雅普诺夫函数方法分析算法的稳定性。最后搭建电动助力制动系统的快速原型试验平台来验证算法的有效性。试验结果表明,采用RBF神经网络滑模变结构的控制策略对电动助力制动系统液压力的控制误差在2%以内,具有良好的控制效果。研究成果为EBooster系统的压力控制提出一种具有良好自适应性的算法设计思路。
