Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems

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.