基于径向基函数神经网络的无人直升机吊装系统滑模减摆控制

针对存在非线性、强耦合、外部未知有界干扰和建模不确定性的平面运动下无人直升机吊装系统,研究了一种基于径向基函数神经网络(radial basis function neural networks,RBFNNs)和干扰观测器的无人直升机吊装系统滑模减摆控制方法。首先将系统模型转换成仿射非线性形式,利用RBFNNs逼近系统不确定性,设计干扰观测器估计神经网络逼近误差与外界未知有界干扰的复合值。然后基于RBFNNs和干扰观测器设计了滑模减摆控制器,并用Lyapunov方法证明闭环系统稳定性;最后通过仿真验证了所设计控制器的有效性。

Sliding Mode Anti-swing Control for Unmanned Helicopter Slung-Load System Based on RBF Neural Networks

Regarding unmanned helicopter slung-load systems which work in plane motion along with nonlinearity, strong coupling, unknown external bounded disturbance and modeling uncertainty, a sliding mode anti-swing control method based on radial basis function neural networks (RBFNNs) and a disturbance observer is proposed in this paper. Firstly, a system model is constructed in a general affine nonlinear form with its modeling uncertainty approximated by the RBFNNs. Secondly, the nonlinear disturbance observer is used to estimate the compound disturbance containing the approximation error of neural networks and external unknown bounded disturbance. Then a sliding mode anti-swing controller is designed based on RBFNNs and the disturbance observer. Furthermore, the stability of the closed-loop system is proved by using Lyapunov function. Finally, numerical simulations demonstrate the effectiveness of the control strategy.