Disturbance observer-based adaptive fixed-time neural control for uncertain unmanned helicopter with system uncertainties and full-state constraints

What is it about?

In this paper, a fixed-time disturbance observer (FTDO) based adaptive neural fixed-time control strategy is designed for the uncertain medium-scale unmanned helicopter with full-state constraints and external disturbances. Firstly, the tan-type barrier Lyapunov function (BLF) and neural network (NN) are constructed to deal with the full-state constraints and system uncertainties, respectively. Subsequently, the compound disturbances are estimated by FTDOs. Simultaneously, the FTDOs and fixed-time control strategy guarantees that the errors of disturbances and states converge to the desired region in fixed-time, and the upper bound on the convergence time of the FTDOs and the designed controller can be estimated through the devised parameters. In addition, Lyapunov stability theory proves that all states of the closed-loop system are semi-globally uniform ultimately bounded. Finally, the control performance of the proposed strategy is verified by the simulation results.

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