Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems
针对二维自由度非线性直升机系统，提出一种基于宽学习神经网络的容错控制方法，通过增量节点和增强节点减小跟踪误差，并设计自适应辅助参数应对执行器故障，仿真和实验验证了有效性。
This study is aimed to design a fault-tolerant control using a broad learning neural network (BLNN) for a two-degree-of-freedom (2-DOF) nonlinear helicopter system. Compared with the conventional radial basis function neural network, the BLNN can approximate uncertainties and unknown functions with smaller tracking errors by adding incremental and enhancement nodes. Considering possible actuator faults in during actual application, an adaptive auxiliary parameter is established to prevent their effects on control. Through direct Lyapunov method, the stability and convergence of the closed-loop system are analyzed. The results from simulations and experiments conducted on a 2-DOF helicopter laboratory platform of Quanser demonstrate the validity and feasibility of the proposed control method.