Reinforcement Learning-based Integrated Position and Attitude Control Method towards Morphing Flight Vehicles
Aiming at the problem that the change of parameters or characteristics of morphing flight vehicle,such as center of mass,aerodynamic forces,moments of inertia,aerodynamic moments,and disturbance rejection capability of the vehicle during flight,which may significantly affect the flight control quality of the vehicle,an integrated position and attitude control algorithm based on reinforcement learning is proposed.The twin-delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm is utilized to train the neural network control laws to achieve integrated position and attitude control for morphing aircraft.The algorithm has been verified through mathematical simulation experiment and the flight test.The results of simulation experiments and flight tests show that the network control law designed with the proposed algorithm can achieve integrated position and attitude control for morphing aircraft,and has strong adaptabilities to external disturbances.
Reinforcement learningMorphing flight vehiclePosition controlAttitude control
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