摘要
由于无人机模型的非线性和参数的时变性,传统PID方法设计的控制器动静态性能可能会变得很差,针对这种问题,设计了适合于某型无人机的模糊自适应姿态控制器;对于模糊控制器的隶属函数选择中盲目性的问题,利用粒子群优化算法对隶属函数进行智能寻优,降低设计过程中专家主观意向对控制器性能不确定性的影响;仿真结果表明,文章设计的控制器相对传统PID有更好的动静态性能,并且对于模型参数的时变性具有一定的鲁棒性。
Due to the nonlinear and the time--varying parameters of the UAV model, controller design of traditional PID method of the dynamic and static performance may become very bad, in order to solve this problem, designing the fuzzy adaptive attitude controller for a certain type of UAV. To avoid the blindness of membership function in fuzzy controller selection problem, this paper uses the particle swarm optimization algorithm of intelligent optimization of membership function, reducing the influence of experts in the design process of the sub- jective intention of controller performance uncertainty. The simulation results show that the static and dynamic performance of the controller designed in this paper compared with the traditional PID is better, and has certain robustness to model time--varying Parameters.
出处
《计算机测量与控制》
2015年第5期1571-1574,共4页
Computer Measurement &Control
关键词
无人机
姿态控制
模糊自适应
粒子群优化
UAV
attitude control
fuzzy adaptive
particle swarm optimization
