Disturbance Observer-Based Neural Network Control of a 2-DOF Helicopter System With Input Saturation and Output Constraints
针对二自由度直升机系统，提出一种结合径向基神经网络和扰动观测器的控制方法，处理输入饱和、外部扰动和输出约束问题，并通过仿真验证有效性。
This article presents a disturbance observer (DO)-based neural network (NN) control for a two-degree-of-freedom (2-DOF) helicopter system with input saturation, external disturbances, and output constraints. First, the uncertainties in the helicopter system are approximated using a radial basis function NN. Subsequently, a DO is used to approximate unknown compound disturbances, involving errors from NN estimation, input saturation, and external disturbances. To address the issue of output constraints imposed at a prescribed time period, a novel time-shift function and an adjusted barrier function are employed. Through the direct Lyapunov method, the boundedness of all control signals in the closed-loop system is verified. Finally, the effectiveness of the proposed control method is validated through numerical simulation results.