Intelligent Attitude Fault-Tolerant Control of Space Tumbling Target Fly-Around Based on RBF Neural Network

The flying around monitoring task of space tumbling target is one the key links its on-orbit service. Considering practical constraints system inertia uncertainty, external disturbance, actuator saturation, and fault in engineering practice, a robust composite controller based on radial basis function (RBF) neural network proposed. First, new line sight rotation (RLOS) coordinate system, relative attitude kinematics dynamics equations between tracker error quaternion are established; second, RBF used to estimate additive multiplicative faults fast nonsingular terminal sliding mode surface (FNTSMS) combined with active disturbance rejection control (ADRC) technology design finite-time fault-tolerant (FTC) strategy high accuracy, strong robustness, anti-saturation network. It proven that designed can ensure state converges small region containing origin limited time under Lyapunov framework. Finally, effectiveness superiority were verified by numerical simulation.