武装直升机航迹规划与轨迹控制研究
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摘要
本文研究了武装直升机航迹规划与轨迹控制系统,给出了该系统的系统结构,并研究了各分系统的主要问题。具体内容包括以下几个方面:
     针对数字地图技术,本文讨论并研究了随机地形算法、山峰模拟算法、分形曲面生成算法,通过这些算法可以得到具有不同统计特性的地形数据;使用基于分形插值曲面算法可以得到不在网格点上的地形数据。针对具体的算例,各种算法都给出了相应的仿真图形。讨论了数字地图数据的存储格式。提出了威胁模型的概念,并且把威胁叠加到数字地图中,把威胁回避转化为地形回避来处理。
     对于航迹优化问题,采用了改进自适应遗传算法,首先把航迹规划问题转化为受约束的最短路径问题,并采用狄克斯特拉算法得到可以进行地形回避和威胁回避的粗略优化路径,然后利用改进的自适应遗传算法对粗略优化路径进行优化,得到优化航迹。开发了航迹规化软件系统,系统运行可靠稳定。
     对于实时航迹规化问题,本文采用了动态路径算法和适应角地形跟随算法生成三维参考飞行航迹,当直升机沿该航迹飞行时,充分利用地形作掩护,同时兼顾了直升机机动能力的限制,使得飞机的任务生存能力得到了提高。
     鉴于直升机模型具有强耦合、不稳定、强非线性的特点,本文建立了直升机非线性运动方程,并对自由飞机的开环特性进行了研究,分析了运动耦合和操纵耦合对直升机各状态的影响,揭露了直升机各通道之间的耦合原因,为下面的控制系统设计提供了一定的物理概念。
     本文对直升机姿态角通道和总距通道分别进行设计。在姿态角通道的设计中,采用了动态逆思想,按照时标分离的原则将直升机飞控系统划分为快慢回路,并采用动态逆方法对飞行控制系统进行设计和仿真,仿真结果表明动态逆在通道解耦方面非常有效。本文还对动态逆的鲁棒性进行了仿真和验证。
     针对模型不精确引起的动态逆控制器的逆误差,本文采用神经网络自适应的在线进行动态补偿。通过基于李亚普诺夫稳定的BP网络进行研究,对神经网络自适应控制律进行详细的分析和讨论,设计了神经网络控制器。对设计的神经网络控制器进行仿真,表明该控制器能够很好的补偿动态逆产生的误差。
     本文最后设计了基于小脑模型神经网络(CMAC)和PID并行控制的轨迹跟踪控制器,与姿态角控制器相结合,完成直升机整个飞行控制系统的设计。仿真结果表明,对于飞行参考航迹,该轨迹跟踪控制器具有很好轨迹跟踪能力,满足轨迹跟踪精度的要求。
Route planning and trajectory control system of the attack helicopter is studied in this dissertation. Divide the system into several sub-systems and study on the main techniques. The main contents are as follows:
    It discusses and simulates several problems of digital map technique in aviation:
     including random terrain, mountain simulation, fractal surface generation, by those we can
    get statistic data of the terrain. We can get the data not on the grid by fractal interpolation
    surface. It discusses data storage format and the conception of threat model, which can add
    the threat to digital map that can transform the threat avoidance to terrain avoidance.
    In the course of the design of trajectory optimization, we adopt the improved adaptive genetic algorithm. First, the route planning problem is converted into the restricted short-cut problem and Dijkstra algorithm is used to get the rough optimized trajectory which makes terrain avoidance and threat avoidance effectively. Then, the adaptive genetic algorithm is used to optimize the reference trajectory. The trajectory planning software is developed which runs credibly.
    This dissertation combines DYNAPATH algorithm with adapt-angle TF algorithm to create the three-dimension reference flight trajectory. This method can enhance attack aircraft survivability and increase strike effectiveness while reducing pilot workload.
    Because the model of helicopter is a highly coupled unstable nonlinear system, a group of nonlinear motion equations are established first, and then analyzed the infection of the couple of motion and the couple of control of the free helicopter. This work supplied the physics concept for the design the flight control system.
    In this dissertation the control loop of attitude angle and collective piston loop are separately. The controller is designed at two scales. And the simulation shows the uncoupling of three attitudes angle loop is achieved with dynamic inversion scheme. It also simulates and tests the robustness of dynamic inversion scheme.
    Adaptive neural network is discussed to compensate the inversion error resulted from inaccurate model of helicopter. By studying the BP network based on Lyapunov stability, we discuss the adaptive control law and design the neural network controller. The simulations show the controller could compensate inversion error effectively.
    Finally, the trajectory tracking controller based on CM AC and PID parallel control is designed, which is combined with attitude angle controller and complete whole flight controller system. To the reference flight trajectory, the controller has good tracking ability and satisfies precision request.
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