BLNN-based adaptive control method for a class of lateral thrust/aerodynamic force composited high-speed UAVs with uncertainties and disturbances under the time-varying output constrains

In this work, a novel broad learning neural network-based adaptive control (BLNNAC) scheme is designed for a class of lateral thrust/aerodynamic force composited high-speed unmanned aerial vehicles with external perturbations, and unknown uncertainty under the time-varying output constraints. The proposed control strategy incorporates several key innovations. Firstly, an innovative tan-type barrier Lyapunov function is introduced to successfully avoid violations of time-varying output constraints. Secondly, the fast response capability and robustness to external disturbances inherent in the integral sliding mode control (ISMC) scheme are integrated into the strategy, enhancing its overall performance. Finally, a novel broad learning neural network (BLNN) is designed to effectively suppress the detrimental effects of unknown uncertainties, thereby significantly improving the system’s approximation performance. The results indicate that all signals are well-constrained, and the transient states of the output signals satisfy the constraint conditions constantly. Finally, the effectiveness and advantages of the proposed scheme are demonstrated through simulation results.