Neural network-based adaptive terminal sliding mode control for the deployment process of the dual-body tethered satellite system

The dual-body tethered satellite system, which consists of two spacecraft connected by a single tether, is one of the most promising configurations in numerous space missions. To ensure the stability of deployment, the radial basis function neural network-based adaptive terminal sliding mode controller is proposed for the dual-body tethered satellite system with the model uncertainty and external disturbance. The terminal sliding mode controller serves as the main control framework for its properties of the strong robustness and finite-time convergence. The radial basis function neural network is adopted to approximate the model uncertainty, in which the weight vector of the radial basis function neural networks and the unknown upper bound of the external disturbance are estimated by using two adaptive laws. Finally, the Lyapunov theory and numerical simulations are used to prove the validity of the proposed controller.