Integral barrier Lyapunov function based ride quality control of flexible civil aircraft with overload constraint

This paper proposes a neural backstepping output-constrained control for the flexible civil aircraft overload tracking. Considering the normal overload constraint caused by ride quality requirements from ISO 2631-1 and MIL-F-9490D, the control laws are designed based on integral barrier Lyapunov function (IBLF). To deal with the model uncertainty and external disturbance, the neural networks and disturbance observer are applied in the controller, while the composite learning technique is utilized to improve the NNs learning performance. The uniformly ultimate boundedness of the system and the normal overload constraint are proved via Lyapunov stability analysis. Numerical and hardware-in-loop(HIL) simulations results show that the controller improves the ride quality effectively under typical discrete gusts or atmospheric turbulence, and the normal overload is limited to the predefined interval.