Adaptive neuro-fuzzy control approach for spacecraft maneuvers

• Main Authors: Abdelrahman, Mohammad, Tantawy, M., Bayoumi, M.
• Format: Article
• Language: English
• Published: ICGST-ACSE 2006
• Subjects: TL787 Astronautics
• Online Access: http://irep.iium.edu.my/23319/1/ACSE-2006.pdf; http://irep.iium.edu.my/23319/; http://www.icgst.com/journals/journal.aspx?subid=39

This paper introduces a new technique to control spacecraft maneuvers. The new technique is based upon using neuro-fuzzy approach to predict the required control torque, using a modelless-strategy, for attitude and rate tracking subjected to torque constraints. The Neuro-Fuzzy Controller (NFC) is built up using the Adaptive Neuro-Fuzzy Inference System (ANFIS) which transforms a fuzzy controller into an adaptive network to take the advantage of all the neural network control techniques proposed in the literature. First, the inverse dynamics of the spacecraft is developed by training the ANFIS with specified states such as Euler angles and the angular velocities. These data can be collected via direct measurements, estimators, or simulation using attitude propagators. Second, three types of controllers are developed, started with a Single Level NFC (SLNFC) to a Multi Level NFC (MLNFC) and ended by a Hybrid Controller. The configuration of the first and second controllers depends on the structure of the data used in the training phase. While, the hybrid controller utilizes the NFC in general to solve the problem of large angles attitude tracking in the absence of the system model and brings the system to a steady state with relatively small errors then, it switches to either a classical or a modern controller to refine the steady state errors. Finally, each one of them is tested against two different controllers belonging to classical and modern control approaches for the purpose of performance evaluation. The first one is a classical PD controller using quaternion feedback, and the other is a Non-Linear Predictive controller (NLP) which is developed to predict the required control action to track a certain trajectory under rate and torque constraints. The developed controllers have shown a competitive performance to that of classical one and the simulation results give neuro-fuzzy control approach an edge over the modern control approaches specially when considering the hard constraint of a modelless spacecraft.