Autonomous Spacecraft Trajectory Correction via the AMDCF Memory-Driven Control Framework An Aerospace Application of Nonlinear Dynamics

This paper presents a novel aerospace application of the Adaptive Memory-Driven Coupling Framework (AMDCF) for autonomous spacecraft guidance. We propose a hybrid control law that integrates a non-Markovian memory kernel (representing historical trajectory deviations) with a derivative damping term (representing reaction control thrusters). When subjected to severe external perturbations (simulating solar wind or thruster anomalies), the AMDCF-PD controller successfully computes corrective accelerations to guide the spacecraft back to its reference orbit. The dynamic calibration factor ensures that the control effort remains bounded, preventing numerical instability and control saturation. This framework offers a robust, bio-inspired alternative to traditional PID controllers in deep space navigation.