四旋翼飞行器系统的自适应神经网络有界H_(∞)跟踪控制

Adaptive neural network bounded-H_(∞) tracking control for the quadrotor UAV system

针对有外部扰动的四旋翼飞行器系统,采用backstepping方法解决四旋翼飞行器系统在有界稳定情况下难以实现的H_(∞)跟踪控制问题.考虑各子系统间的耦合项、空气阻力和外界干扰对四旋翼飞行器系统产生的影响,设计四旋翼飞行器系统的自适应神经网络有界H_(∞)跟踪控制器.仿真结果表明:设计的控制器能保证四旋翼飞行器系统较精确地跟踪参考轨迹,且对外界干扰具有有界H_(∞)抑制性能.研究结果能为四旋翼飞行器系统的抗干扰设计提供理论指导.

For the quadrotor unmanned aerial vehicle(UAV)system with external disturbances,the backstepping method was used to solve the problem that it was difficult to realize the H_(∞) control of the quadrotor UAV system under the bounded stable condition.Considering the influence of coupling terms among the subsystems,the influence of air resistance and the effect of the external disturbances on the quadrotor UAV system,the adaptive neural network bounded tracking controllers for the quadrotor UAV system were designed.The simulation results showed that the designed controller could ensure the quadrotor UAV system to track the reference trajectory exactly,and it had bounded-H_(∞) suppression performance against external interference.The research results could provide theoretical guidance for the anti-interference design of the quadrotor UAV system.