Aerospace Instrument-Making Annotation << Back Robust Multi-Stage Nonlinear Model Predictive Control for Disturbance Mitigation in a 2-Axes Gimbal System Kafa W., Gavrilov D.A., Buzdin V.E., Tatarinova E.A., Fateev A.S., Merkelov A.A., Sichkar D.S., Zinovchik O.Yu., Potkin O.A. Sensor stabilization is crucial for gimbal systems in aerospace applications to ensure accurate image processing and target tracking. This study introduces a robust nonlinear model predictive control (NMPC) approach for achieving effective sensor stabilization in 2-axes gimbal systems mounted on unmanned aerial vehicles (UAVs). The system's complex nonlinear and time-varying nature is simplifi ed by approximating it around a non-univocal equilibrium point. By formulating the control problem as an optimization task using multi-stage stochastic programming, uncertainties are incorporated through the scenario tree concept, implemented using the do-MPC toolbox based on CasADi/IPOPT. This enables adaptive control inputs and closed-loop operation based on real-time information. Simulation results demonstrate the effi cacy of the proposed approach in stabilizing the system and mitigating disturbances. The controller displays robust behavior across diverse conditions, effectively countering external disturbances and measurement noise. The approach offers fl exibility in tuning hyperparameters to achieve a balance between accuracy and computational requirements. However, a trade-off is observed, with an increase in computational demands as the robust horizon expands. The fi ndings provide valuable insights into system performance and emphasize the infl uence of hyperparameter selection. This research establishes a solid foundation for the design of advanced stabilization systems in aerospace applications, keeping pace with advancing sensor capabilities. Future investigations can focus on refi ning real-time execution and addressing computational complexities to further improve system performance using neural networks. Keywords: Multi-Stage robust nonlinear model predictive control, Disturbance mitigation, Gimbal stabilization, Parameters uncertainty. Pp. 17-36.