BP 神經網絡已被廣泛應用于PID 控制器的優化調參,但這種調參方法具有收斂速度慢、
學習時間長、連接權重初值為隨機值、易于陷入局部極小等缺點。本文提出了一種不同于用BP 網絡調整PID 參數的新的融合方法：PID 神經網絡控制器（PIDNN）；該控制器不僅能克服以上缺點,而且具有很好的魯棒性。本文對PIDNN 在某無人機姿態控制系統的應用進行了仿真研究,仿真結果表明該控制器能夠大大地改善姿態控制系統性能。
關鍵詞：飛行控制 姿態控制 PID 神經網絡控制器

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