Anti-Saturation Quantization Control for Quadrotor Attitude: Dynamic Surface-Based RBF Adaptive Approach

For quadrotor unmanned aerial vehicle attitude dynamics with control input constraints, a dynamic surface tracking control method incorporating a quantizer and anti-saturation is proposed. First, the quadrotor UAV attitude system is transformed into a constrained model using hyperbolic tangent functions and hysteresis quantizers. Subsequently, a backstepping controller is designed by integrating dynamic surface control techniques to achieve attitude control while ensuring stable tracking. Simultaneously, a Radial Basis Function neural network combined with adaptive techniques is employed to compensate for unknown disturbances. Then, a Lyapunov function is constructed for stability analysis, demonstrating that all closed-loop signals are uniformly ultimately bounded. Finally, numerical simulations in MATLAB validate the effectiveness of the designed controller.