小型无人机飞行控制系统设计及其自适应方法研究

发布时间：2018-01-17 18:18

本文关键词：小型无人机飞行控制系统设计及其自适应方法研究　出处：《北京理工大学》2016年硕士论文　论文类型：学位论文

【摘要】：小型固定翼无人机的飞行控制可以分成三个阶段:即自主起飞阶段,自动巡航阶段和自主降落阶段。这三个阶段是个复杂的控制过程,在这个控制过程中涉及到无人机的姿态控制,高度与速度控制以及无人机的导航控制。然而在每一个飞行阶段都存在着未知干扰和参数不确定性,所以设计可靠的飞行控制系统变得尤为重要。本文设计了可靠的自动驾驶仪硬件系统,并且在自动驾驶仪中实现了基本的PID控制方法。除此之外,还进一步进行了一种高效的L1自适应控制方法在无人机飞行控制中的应用研究,解决了在飞行控制过程中一些参数不确定性和未知干扰的影响,并且通过李雅普诺夫稳定性理论证明了算法的稳定性。主要研究内容如下:首先,利用当前先进的传感器以及其他可靠的芯片设计了一款自动驾驶仪系统,通过调试和相关的实验测试满足本文实验用的小型无人机的各个控制要求。其次,通过对无人机飞行原理的分析,在自动驾驶仪中实现了基于PID的无人机自主起降,捷联式惯性导航控制,姿态控制等。在实现PID控制方法的基础上,针对PID控制算法对参数不确定性和未知干扰及非线性特性的控制缺陷,在L1自适应方法的基础上结合极点配置控制算法和BGF(Bounded-Gain Forgetting)算法提出了一种高效的L1自适应控制方法(High Performance L1 Adaptive Control,HPL1AC)。高效的L1自适应控制算法解决了无人机飞行控制系统中的参数不确定性和未知干扰问题,尤其是时变的BGF自适应增益使得算法在非零初始状态下有更好的未知参数估计能力。然后给出了该算法的基于Lyapunov稳定性证明,并且将本文提出的算法通过球杆系统控制器设计实验与PID控制器进行了比较,验证了该算法的较好的控制性能。除此之外,还将高效的L1自适应控制算法应用在无人机纵向模型控制仿真。通过对无人机纵向通道姿态角的控制仿真和无人机自主起降过程的仿真更进一步验证了算法的有效性。最后将高效的L1自适应控制方法与输出反馈相结合,提出一种高效的L1自适应滤波器,可有效的滤除干扰,通过仿真实验验证了算法的有效性。
[Abstract]:The flight control of a small fixed-wing UAV can be divided into three stages: autonomous take-off phase, automatic cruise phase and autonomous landing stage, which are complex control processes. In this control process, the attitude control, altitude and speed control and navigation control of the UAV are involved. However, there are unknown disturbances and parameter uncertainties in each flight phase. So it is very important to design a reliable flight control system. This paper designs a reliable autopilot hardware system and realizes the basic PID control method in autopilot. Furthermore, an efficient L1 adaptive control method is applied to UAV flight control, which solves the effects of uncertain parameters and unknown disturbances in flight control process. And the stability of the algorithm is proved by Lyapunov stability theory. The main research contents are as follows: firstly, an autopilot system is designed using advanced sensors and other reliable chips. Through debugging and related experimental tests to meet the control requirements of the small UAV used in this paper. Secondly, through the analysis of the flight principle of UAV. Autonomous takeoff and landing of UAV based on PID, Strapdown inertial navigation control, attitude control and so on are realized in autopilot. Based on the realization of PID control method. The control defects of PID control algorithm for parameter uncertainty, unknown disturbance and nonlinear characteristics are discussed. Combining pole placement control algorithm with BGF(Bounded-Gain Forgeting based on L1 adaptive method. An efficient L1 adaptive control method, High Performance L1 Adaptive Control, is proposed. The efficient L1 adaptive control algorithm solves the problem of parameter uncertainty and unknown disturbance in UAV flight control system. In particular, the time-varying BGF adaptive gain makes the algorithm have a better ability to estimate unknown parameters in the non-zero initial state. Then, the proof of the algorithm based on Lyapunov stability is given. In addition, the proposed algorithm is compared with the PID controller through the design experiment of the ball rod system controller, which verifies the better control performance of the algorithm. An efficient L1 adaptive control algorithm is also applied to the simulation of UAV longitudinal model control. The algorithm is further verified by the simulation of the attitude angle of the longitudinal channel and the autonomous take-off and landing process of the UAV. Finally, the efficient L1 adaptive control method is combined with the output feedback. An efficient L1 adaptive filter is proposed, which can effectively filter interference. The simulation results show that the algorithm is effective.
【学位授予单位】：北京理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：V279;V249

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