Advanced Control Strategies for Space Systems: Integration of Model Predictive Control and Neural Networks

Abstract

This chapter presents advanced control methodologies for space systems, focusing on the integration of nonlinear model predictive control (NMPC) and neural network approaches. The chapter synthesizes novel developments in controlling coupled structural-attitude dynamics of spacecraft with flexible appendages and multiple robotic manipulators. Key innovations include the application of nonlinear autoregressive exogenous model (NARX) neural networks for adaptive state estimation, passivity-based NMPC for robust control, and piezoelectric actuator integration for precise vibration suppression. The chapter provides comprehensive coverage of mathematical modeling, control algorithm development, and practical implementation considerations. Simulation results demonstrate superior performance compared to conventional approaches, particularly in handling model uncertainties and disturbances while maintaining strict bounds on actuator saturation limits. The methodologies presented are directly applicable to emerging space applications including on-orbit servicing, assembly, and debris removal.