基于PID神经网络控制器的飞控系统设计Design of F

BP 神经网络已被广泛应用于PID 控制器的优化调参,但这种调参方法具有收敛速度慢、
学习时间长、连接权重初值为随机值、易于陷入局部极小等缺点。本文提出了一种不同于用BP 网络调整PID 参数的新的融合方法：PID 神经网络控制器（PIDNN）；该控制器不仅能克服以上缺点,而且具有很好的鲁棒性。本文对PIDNN 在某无人机姿态控制系统的应用进行了仿真研究,仿真结果表明该控制器能够大大地改善姿态控制系统性能。
关键词：飞行控制 姿态控制 PID 神经网络控制器

Abstract: The neural network with BP algorithm has these disadvantages, such as slow
convergence rate, long learn time, local optimization and random value for the initial weight of the net, although it has been widely applied to regulate the parameters of PID controller. A new type of controller called PIDNN, which coalesces traditional PID and neural network together, is introduced in this paper. It overcomes these shortcomings mentioned and has better robustness. Then it is used in the flight control system of UAV, the simulation results show that it improves the performance of the system effectively.
Keywords: Flight Control, Attitude Control, PIDNN