Autolanding remains a challenging task due to changing environmental conditions and the sensitivity of conventional control methods to model uncertainties and delays. This work investigates the application of Incremental Nonlinear Dynamic Inversion (INDI) to an autoland scenario using the PH-LAB Cessna Citation aircraft model. INDI reduces the model dependency by utilising sensor-based feedback, thereby enhancing robustness to modelling inaccuracies and external disturbances. Emphasis is placed on addressing variable sensor and actuator delays, which can degrade control accuracy and stability. The proposed INDI controller integrates multiple feedback loops and Pseudo-Control Hedging (PCH), complemented by a
hybrid filter for angular acceleration estimation and a novel altitude estimator. The latter utilises linear accelerations measured by the IMU, which are subject to smaller delays than altitude measurements from the Digital Air Data Computer (DADC). This estimate is fused with the DADC altitude through a Kalman filter for improved accuracy and mitigation of cumulative errors. Simulation results demonstrate that the controller achieves safe landings under external disturbances and variable delays. Sensitivity analyses further show consistent performance across multiple scenarios, while identifying the controller’s operational
limits.