Article Title

Neural-network-based terminal sliding-mode control for thrust regulation of a tethered space-tug


tethered space-tug, thrust regulation, flexible tether, recursive dynamics algorithm, neural network


This paper studies the thrust regulation of the tethered space-tug in order to stabilize thetarget towed by a flexible tether. To compromise between model accuracy and simplicity,a rigid–flexible coupling multi-body model is proposed as the full model of the tetheredspace-tug. More specifically, the tug and the towed target are assumed as rigid bodies,whereas the flexible tether is approximated as a series of hinged rods. The rods are assumedextensible but incompressible. Then the equations of motion of the multi-body system arederived based on the recursive dynamics algorithm. The attitude motion of the towedtarget is stabilized by regulating the thrust on the tug, whereas the tether-tension-causedperturbation to the tug’s attitude motion is eliminated by the control torque on the tug. Theregulated thrust is achieved by first designing an optimal control trajectory considering thesimplified system model with constraints for both state variables and control input. Thenthe trajectory is tracked using a neural-network-based terminal sliding-mode controller. Theradial basis function neural network is used to estimate the unknown nonlinear differencebetween the simple model and the full model, while the terminal sliding-mode controllerensures the rapid tracking control of the target’s attitude motion. Thrust saturation andtether slackness avoidance are also considered. Finally, numerical simulations prove theeffectiveness of the proposed controller using the regulated thrust. Without disturbingorbital motion much, the attitude motion of the tug and the target are well stabilized andthe tether slackness is avoided.


Tsinghua University Press