Bounded barrier Lyapunov function-based neural network control of space robot with output tracking error constraints...

This paper proposes a control strategy based on Bounded Barrier Lyapunov Function neural network (BBLF-NN) to solve the problems of output tracking error constraints, input saturation and joint input dead zones of a space robot simultaneously. Firstly, with regard to the output tracking error constraints, a novel Bounded Barrier Lyapunov Function (BBLF) method has been developed. An adaptive control scheme based on BBLF has also been designed. Additionally, an assistant system is incorporated into the control system on the basis of an adaptive controller to address the input saturation effect. A radial basis function neural network (RBFNN) is employed to approximate the model and inertia parameter uncertainties. Furthermore, the neural network effectively compensates the joint input dead zone. This control strategy effectively addresses input saturation and compensates for joint input dead zones and inertia parameter uncertainties, thereby improving control accuracy and stability. Numerical simulations conclusively validate the algorithm's efficacy.