Adaptive Neural Network-Based Fixed-Time Trajectory Tracking Control of Space Robot with Uncertainties and Input Nonlinearities

Abstract

In this paper, a fixed-time control strategy based on neural networks is proposed for a space robot with an input dead zone. First, a model-based control method is proposed based on the fixed-time convergence framework. Due to internal errors and external environmental disturbances, the inertial parameters of dynamic models generally exhibit uncertainties, and model-based control methods may exhibit deviations in trajectory tracking. In order to counteract the adverse effects of uncertain inertial parameters on the system and ensure the stability of the control system, an adaptive learning control method based on neural networks is further proposed. To enhance the learning rate of neural networks and achieve the convergence of neural weights within a fixed time, a neural network update rate combined with virtual control rate is proposed. In addition, considering the issue of the joint input dead zone affecting the precision and stability of the space robot, a novel adaptive law is proposed in conjunction with system error signal feedback to mitigate adverse effects. According to the Lyapunov stability theory, the stability of the closed-loop system is proven, with the trajectory tracking error converging to a small neighborhood around zero. Finally, numerical simulation results demonstrate the effectiveness of the control algorithm.

Keywords

• space robot
• fixed-time control
• RBF neural network
• system uncertainty
• input dead zone