动态矩阵控制在飞翼无人机自动着陆时的应用

进场着陆是飞行的复杂阶段,虽然仅占整个飞行的2%～3%,却大约有1/3的飞行事故发生在此阶段。无尾飞翼无人机着陆下滑时对飞机的速度和姿态具有很高的精度要求,但有时仅靠油门控制飞行速度不能满足要求。针对这一情况设计了一种升降舵加阻力方向舵模型预测控制系统。先采用PID控制加快被控对象的响应速度,在此基础上建立基于动态矩阵控制(DMC)算法的模型预测控制器。DMC的在线优化和反馈校正等特点有效地提高了系统的整体性能。仿真结果表明,与经典PID控制器相比,该系统能够更好地跟踪下滑轨迹,提高无人机的动态响应,并且严格控制下滑速度。

Application of Model Predictive Control (MPC) on Aircraft ALCS

Approaching landing is the complex phase of the flight, though only accounted for the entire flight of the 2%~3%, about one-third flight accident occurs in this phase. Present aircraft ALCS (automatic landing control system), usually employing PID control, appears unable to meet the high landing precision required by tailless flying wing UAV. Especially sometimes using engine thrust control, flight speed is still unable to meet the requirements. Then, model predictive control system of elevator and drag-rudder will be designed by means of controlling airspeed. PID controller accelerates the speed of response of the controlled object, model predictive controller based on Dynamic Matrix Control(DMC) algorithm effectively increases the overall performance of the system for its on-line optimization and feedback features. Simulation results indicate that the model predictive control improves system performance and stability of UAV compared to PID control.