A Reinforcement Learning‐Based Nonlinear Trajectory Tracking Control Strategy for Space Flexible Manipulator With LuGre Friction Compensation

High‐precision trajectory tracking control of space flexible manipulator represents a significant research focus of contemporary research and poses great challenges in both academia and engineering. To address the issue of low control precision in space flexible manipulator, which arises from highly nonlinear dynamics in complex spacecraft environments, the LuGre friction model is incorporated into the dynamic equation to improve the accuracy of frictional dynamic behaviour modelling. Subsequently, a reinforcement learning‐based sliding mode control (RL‐SMC) method is developed to achieve precise approximation and compensation of uncertain nonlinearities within the space flexible manipulator system. The employed RL framework is based on the actor–critic architecture, where the actor neural network generates the control policy, whereas the critic neural network evaluates the policy and continuously provides feedback regarding the system state. This control method uses a radial basis function neural network (RBFNN) combined with the SMC to minimise approximation error. In complex space environments, the actor–critic framework enhances the approximation of nonlinear dynamics for a space flexible manipulator and facilitates more efficient adaptation to variations in system dynamics. In addition, joint angle output constraints are implemented to manage the restricted motion of the space flexible manipulator in confined workspaces, aiming to prevent collisions during operation and avoid structural damage. Finally, the stability of the closed‐loop system is rigorously established using the Lyapunov stability theory. Numerical simulations demonstrate the efficacy of the proposed approach in improving both control precision and environmental adaptability of the space manipulator.