Adaptive Neural Network-Based Fault-Tolerant Control of 2-DOF Helicopter With Output Constraints

Abstract

In this paper, we propose an adaptive neural network-based fault-tolerant control for the two-degree of freedom (DOF) helicopter system with actuator fault and output constraints. First, the radial basis function neural network is used to estimate the uncertainty of the system. Moreover, adaptive auxiliary parameters are used to compensate the actuator failure. And then, the barrier Lyapunov function is adopted to deal with the output constraints in the system. By analyzing the stability of Lyapunov function, it is strictly proved that the closed-loop system is semi-globally uniform and bounded, and under the combined action of actuator fault and output constraints, accurate tracking control performance is achieved. Finally, the simulation results in the 2-DOF helicopter system show the effectiveness of the control strategy.