基于神经网络的拖靶高度自抗扰控制研究

为了解决建立拖靶数学模型的难点以及高度测量误差的影响，提出了基于神经网络的拖靶高度自抗扰控制方法。自抗扰控制降低了模型的不准确对飞行控制造成的影响。以垂向加速度信号和高度信号作为神经网络的输入信号，得到自抗扰控制器的补偿因子系数，既降低了高度表测量误差的影响，又使得自抗扰控制的补偿因子得到实时更正。仿真表明，该方法使得拖靶的控制性能更加精确，有效抑制了外界的干扰，达到了恒高飞行的目标。

Research about Active Disturbances Rejection Control of Tow Target Height based on Neural Network

In order to solve the difficulties to establish the mathematical model of tow target and the influence of height measurement error,active disturbances rejection control method based on neural network is proposed. The influence of mathematical model inaccuracy to the flight is reduced by design of active disturbances rejection control. With height and vertical acceleration as the input signals to neural network,the compensation for disturbance rejection control factor coefficient is got,which decreases the influence of height measurement error,and makes the compensation of controllor be corrected in real time. Simulation indicats that this method makes the flight performance of the tow target more accurate,effectively restrains the external interference,and accomplishes goal of a constant high flying.