Neural Robust Adaptive Sliding Mode Method for Spacecraft Attitude Control with Input Saturation
For the problems of actuator saturation,uncertain inertial parameters and unknown external disturbances in the process of spacecraft attitude tracking control,a robust adaptive radial basis function neural network(RBFNN)enhanced sliding mode control method is proposed.Firstly,a quaternion-based model of the spacecraft's attitude kinematics and dynamics is established,and a disturbance observer is established for unknown external disturbances.Secondly,to solve the actuator saturation problem,a Gaussian error function is introduced to constrain the controller amplitude,and a PID sliding mode control framework is used to design the controller.In the controller design process,a novel switching function is used to combine robust adaptive control and RBFNN to approximate uncertain inertial parameters.The gradient descent method is employed to solve the weight optimization problem of RBFNN.Subsequently,the boundedness of the closed-loop system is proved based on Lyapunov theory,and the convergence domain of the closed-loop system is analyzed.Finally,simulation analysis is conducted to verify the effectiveness and robustness of the designed controller.
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维普
