Finite-Time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network

Abstract

In this paper, a composite finite-time prescribed performance tracking control scheme is presented for an unmanned helicopter (UH) system subject to performance constraints, model uncertainties and external perturbations. A new finite-time neural network disturbance observer (FTNNDO) with adaptive laws is designed to deal with the external disturbances and model uncertainties, which not only accelerate the convergence rate in finite time but also eliminate the complicated differential calculation in the traditional backstepping scheme. Using the continuous adaptive law, the neural network (NN) approximate errors can be effectively estimated and compensated online without the chattering and gain overestimation caused by traditional methods, thus further enhancing the robustness of the system. To constrain the tracking performance of the transient process and steady-state accuracy, a novel prescribed performance function is designed to preset the tracking errors within prescribed boundaries. Based on the FTNNDO and barrier Lyapunov function (BLF), an improved finite-time tracking controller is designed to achieve fast convergence with prescribed performance. By using Lyapunov synthesis, it is strictly proven that the finite-time convergence of the closed-loop control system can be achieved and tracking errors are always within the prescribed performance bounds. In the end, simulation results for the UH tracking control system are given to demonstrate the effectiveness of developed control scheme.