基于HOD的无模型四旋翼RBF滑模控制

为了探索解决在无模型控制算法中如何对系统的未知模型和扰动进行准确估计,提出一种基于高阶微分器(HOD)的无模型RBF神经网络滑模控制器(HODRBFSMC).引入HOD估计系统模型的各阶状态变量,并将系统模型的未知项和外界干扰统一归为总扰动,通过RBF神经网络对总扰动进行估计,并根据Lyapunov定理证明所设计控制器的闭环稳定性.为验证控制器的有效性,所设计的控制器被应用于四旋翼飞行器的轨迹控制,解决其模型参数复杂且飞行过程中易受外界干扰的问题.仿真实验表明,所提出方法能够有效估计并补偿总扰动,其轨迹跟踪能力和抗干扰性能相比PID和高阶微分反馈控制(HODFC)具有一定的优越性,能够很好地满足四旋翼飞行器控制的需求.

Model free RBF sliding mode control based on HOD designed for quadrotor

For the problem that how to accurately estimate the unknown model and disturbance of the system in model free control, a model-free RBF neural network sliding mode controller(HODRBFSMC) based on a high-order-differentiator(HOD)is proposed. Firstly, the HOD is introduced to estimate the state variables of each order of the system model. The unknown items of the system model and external disturbances are unified as total disturbances, and the total disturbances are estimated by the RBF neural network. According to the Lyapunov theorem, the closed-loop stability of the designed controller is proved. In order to verify the effectiveness of the controller, the designed controller is applied to the trajectory control of the quadrotor to solve the problem of complex model parameters and susceptibility to external interference during flight. The simulation experiments show that the proposed method can effectively estimate and compensate the total disturbances, and its trajectory tracking ability and anti-disturbance performance are superior to PID and high-order-differentaitor feedback control(HODFC), and can well meet the needs of quadrotor control.