Neuro-Learning Based Fault-Tolerant Control of a 2-DOF Helicopter System With Sensor Fault and Unknown Dead Zone

Most existing control methods for nonlinear systems rely on adaptive parameters or assume full-state measurability, with limited attention paid to scenarios involving unmeasurable system states. To overcome these shortcomings, this study proposes a neuro-learning based fault-tolerant control strategy for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system with sensor gain faults and an unknown dead zone. First, to address the inaccuracy of state measurements caused by sensor gain faults, a state observer is designed to reconstruct the system states, and adaptive parameters are introduced to estimate the fault in real time, providing compensation information for the controller design. A radial basis function neural network (RBFNN) is employed to address the uncertainties in the nonlinear helicopter system. In addition, the RBFNN, adaptive parameters, and bounded estimation are combined to compensate for the effects of the unknown dead zone. The stability and convergence of the closed-loop system are analyzed using the direct Lyapunov method. In simulations, the observer accurately estimates the actual system states before sensor faults occur, and it responds rapidly and reestablishes accurate state estimation when a fault is introduced. In an experimental validation using a Quanser 2-DOF helicopter platform, the proposed control method provides improved tracking performance and enhanced robustness.