Adaptive neural network control of an uncertain 2-DOF helicopter system with input backlash and output constraints

This study considers an adaptive neural control for a two degrees of freedom helicopter nonlinear system preceded by system uncertainties, input backlash, and output constraints. First, a neural network is adopted to handle the hybrid effects of input backlash nonlinearities and system uncertainties. Subsequently, a barrier Lyapunov function is introduced to limit the output signals for further ensuring the safe operation of the system. The bounded stability of the closed-loop system is analyzed employing the direct Lyapunov approach. In the end, the simulation and experiment results are provided to demonstrate the validity and efficacy of the derived control.

Automated summary

This study proposes an adaptive neural control for a two degrees of freedom helicopter nonlinear system, which takes into account system uncertainties, input backlash, and output constraints. A neural network is used to handle the hybrid effects of input backlash nonlinearities and system uncertainties, followed by the introduction of a barrier Lyapunov function to limit the output signals for safe operation. The closed-loop system's bounded stability is analyzed using the direct Lyapunov approach. Simulation and experiment results demonstrate the effectiveness and efficacy of the derived control.