ZMP-Guided Ground Anchoring for Quadrotor Landings Using MRAC-Neural Networks

Landing underactuated quadrotors on inclines is challenging as sudden contact transients convert translational kinetic energy into critical edge-tipping moments. Conventional controllers struggle to mitigate these sub-second impacts without computationally intensive prior terrain models or payload-restricting mechanical shock absorbers. This paper introduces a stabilization approach combining a Zero Moment Point (ZMP)-guided anchoring strategy with an impact-resilient Model Reference Adaptive Control Neural Network (MRAC-NN). The ZMP serves as a proactive geometric threshold. Evaluating overturning moments directly in the body frame before physical tilt occurs triggers immediate asymmetric bidirectional thrust. This artificially augments the surface normal force, thereby securing the vehicle on steep inclines that exceed natural static friction limits. Concurrently, the computationally efficient MRAC-NN suppresses unstructured aerodynamic disturbances and high-frequency impact shocks. To prevent parameter wind-up during sudden kinematic arrest at touchdown, the neuro-adaptive law is structurally augmented with robust bounding and dynamic state-locking mechanisms, mathematically guaranteeing Uniform Ultimate Boundedness (UUB). The framework is validated through high-fidelity simulations that incorporate Linear Complementarity Problem (LCP) rigid-impact constraints and stick–slip friction. Results demonstrate the architecture effectively suppresses post-impact bouncing and transforms critical tipping moments into controlled planar slides, reducing stabilization penalties by up to $47.5\%$ and expanding the survivable flight envelope to ${60}^{\circ }$ inclines where classical methods fail.