Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints

This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints, deck motion, measurement noises, and unknown disturbances. The iterative model predictive control (MPC) strategy with constraints is proposed for automatic landing control of the aircraft. First, the long short-term memory (LSTM) neural network is used to calculate the adaptive reference trajectories of the aircraft. Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator. Second, an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory. Moreover, some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC. Finally, the simulation results of F/A-18A aircraft show that compared with the conventional MPC, the presented MPC strategy improves the computational efficiency by nearly 56% and satisfies the control performance requirements of carrier landing.