Adaptive Motion Control of Non-affine Unmanned Hovercraft Under Asymmetries Constraints

Addressing the issue of non-affine motion control for unmanned hovercraft operating at high speeds while taking safety into account. A novel algorithm based on asymmetrical integral barrier Lyapunov function (IBLF) and neural network is proposed. Firstly, the non-affine model of the surge speed and yaw subsystem of the unmanned hovercraft is analyzed. By utilizing neural networks, the implicit function theorem is used to demonstrate that there is a single solution to the estimate of ambiguous parameters of control inputs. Additionally, to ensure that yaw angular velocity maintains a pre-specified safety threshold at high speed and that the surge speed performance necessary, a time IBLF is introducing to designed the surge speed and yaw controller. Furthermore, all tracking errors are ultimately uniformly bounded, according to the Lyapunov stability theory. Finally, simulation results confirm the suggested controller’s effectiveness and robustness.