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翼伞系统建模控制及规划方法研究
Alternative TitleModeling, Control and Planning of Parafoil System
李兵兵1,2
Department机器人学研究室
Thesis Advisor韩建达
Keyword翼伞系统 简化模型 主动模型 能量反馈 末端降落规划
Pages128页
Degree Discipline模式识别与智能系统
Degree Name博士
2019-05-25
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract本文针对翼伞系统的简化模型建立、模型在线修正的主动建模、控制器设计以及降落规划问题展开研究。主要研究内容如下:首先,基于伞体的空气动力学方程,通过添加在负载的发动机推力,建立了针对有动力翼伞系统的六自由度非线性初始模型;针对得到的初始模型,通过特定平台实验数据的相关性分析,得到了适用于特定飞行平台的相关性结论,并用于指导初始模型的简化,进而得到系统的标称模型。然后,对于得到的系统标称模型,通过将系统的模型漂移、未建模因素和外界干扰等对系统状态的影响,视为系统的过程噪声,并通过实时模型误差估计,建立了系统的主动模型。对于线性标称模型,通过线性卡尔曼滤波器,实现了在线模型差的估计,进而建立了线性主动模型;对于非线性标称模型,通过扩展卡尔曼滤波器,建立了非线性主动模型。通过在不同飞行状态和包含外界气流干扰情况下的飞行实验,验证了模型的有效性。再次,对于得到的系统模型,通过对系统输入、状态与系统能量之间的分析,提出了基于能量反馈的控制方法,并基于此方法,采用前视引导策略,完成了系统对目标轨迹的跟踪。在无系统输入和不考虑外界风扰的情况下,系统将保持当前的运动状态。对于系统的输入,发动机的推力能够直接影响系统的前向速度和高度,并直接改变系统的能量;双侧拉输入通过实现系统前向速度的减小和高度的增加,实现系统动能与势能的转化,并保持系统能量总和的不变;单侧拉输入直接改变系统的航向角度,并对系统的前向速度和高度不产生重要影响。通过使用单通道PID实现系统的高度控制;通过在前向速度通道添加能量反馈,实现了能量的控制,从而实现系统前向速度的调节。仿真结果表明,相较于独立通道PID,该方法具有明确的调节过程和更好的控制效果,且有利于系统整体的稳定。前视引导策略通过将飞机前方最近的期望轨迹线上的点作为当前飞行目标点,能够有效实现对多边飞行轨迹的跟踪。在仿真中,通过实现系统对多边形目标曲线的跟踪,验证了该方法的有效性。最后,对于翼伞系统特殊的降落问题,通过对贝塞尔曲线对降落阶段的末端轨迹进行改造,将期望轨迹的求解问题转化为代价函数的优化求解问题,并通过将环境信息、系统动力学约束以及降落误差等信息融入到系统的代价函数中,最终得到了在保证系统动力学约束情况下,能够实现对外界障碍物躲避和保证降落精度的可行期望轨迹,最终在仿真环境中,通过不同环境下的飞行测试验证了方法的有效性。本文的研究工作包含了系统的简化模型建立、包含模型误差的实时动力学建模、基于能量反馈的控制器设计和基于贝塞尔曲线的降落规划算法四个方面的内容,并通过仿真和实验测试,对方法的有效性进行了验证。
Other AbstractThis paper focuses on the establishment of simple model of parafoil system, active modeling including external disturbance estimation, controller design based on the energy feedback, and landing strategy. The main research contents are as follows: Firstly, on the basis of the traditional unpowered parafoil system model, the six-degree-of-freedom nonlinear initial model of the powered parafoil system is established by adding the engine thrust on the payload. For the initial model obtained, the system correlation conclusions applicable to different flight states are obtained through the analysis of experimental data of our specific platform and used for the simplification of the initial model to obtain the system’s nominal model. Then, for the obtained system nominal model, the system’s model drift, unmodeled factors and external disturbance are regarded as the process noise of the system, and the active model of the system is established through the real-time model error estimation. For linear and nonlinear nominal models, the system state and model error are estimated in real time through linear Kalman filter and extended Kalman filter, respectively. Thirdly, for the obtained system model, the control method based on energy feedback is proposed through the analysis of the system state, input and system energy. Based on this method, the forward guidance strategy is adopted to realize the system's tracking of the desired path. In the system characteristics represented by the linear model, the system will maintain the current state of motion in the absence of system input and external disturbance. For the input of the system, the thrust of the engine can directly affect the forward speed and height of the system, and thus change the energy of the system. By reducing the forward velocity and increasing the height of the system, the symmetric brake input can transform the kinetic energy and potential energy of the system and keep the total energy of the system unchanged. The assymmetic brake input directly changes the heading angle of the system and has no significant effect on the forward speed and height of the system. A single-channel PID controller is utilized to achieve the system height control and a controller based on energy feedback to achieve the system energy control, so as to achieve the system forward speed regulation. The simulation results and the comparison with the normal PID controller based on two independent channels verify the rationality and effectiveness of the energy-based controller. By taking the point on the expected trajectory line in front of the aircraft as the current flight target, the forward guidance strategy can effectively track the multilateral flight trajectory. Finally, based on Bezier curves, the obtaining problem of desired trajectory is transferred into an optimization problem by introducing the environmental information, system dynamics constraint and landing error into the cost function. In this way, a feasible trajectory can be derived while taking account of system dynamics constraints, obstacles avoidance and landing accuracy. Flight tests have been carried out to verify the effectiveness of the guidance method.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/25152
Collection机器人学研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
李兵兵. 翼伞系统建模控制及规划方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2019.
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