Model predictive control for Takagi-Sugeno fuzzy model-based Spacecraft combined energy and attitude control system

A Combined Energy and Attitude Control System (CEACS) is a dual system in which flywheels are used as energy storage and atti-tude control devices. This work is a progress on CEACS for small satellites to improve the attitude accuracy. In this maiden work, the Fuzzy-MPC controller is introduced to regulate the CEACS attitude with both linear and nonlinear initial angles, and in the presence of actuator constraints. The nonlinear attitude model is transformed into a set of linear subsystems using the Takagi-Sugeno fuzzy mod-eling approach. The subsystem is designed with a MPC controller and a total control action is summed up using the parallel distributed compensation approach. The numerical results have been analyzed in terms of attitude pointing accuracies, actuator constraints, and energy consumed by the actuators. A performance comparison between the Fuzzy-MPC and Fuzzy-LQR controllers has also been done. The results validate that the Fuzzy-MPC controller achieves the desired CEACS attitude pointing accuracy of 0.0010 degrees with a zero steady-state error and keeps the control torques within the actuator constraints. On the other hand, the Fuzzy-LQR controller gives some measurable steady-state error and achieves the CEACS attitude pointing accuracy of 0.0012 degrees. Therefore, the Fuzzy-MPC based CEACS architecture not only achieves the desired pointing accuracies but also produces ten times smaller control torques than the Fuzzy-LQR controller, which result in a lower onboard power consumption. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

Automated summary

This study presents a combined energy and attitude control system (CEACS) utilizing flywheels for energy storage and attitude control. The fuzzy-model predictive controller is used to regulate the CEACS attitude, considering both linear and nonlinear initial angles and actuator constraints. The results validate that the fuzzy-model predictive controller achieves the desired pointing accuracy and reduces control torques and energy consumption.