Dissertation/Thesis Abstract

Spacecraft Formation Control: Adaptive PID-Extended Memory Recurrent Neural Network Controller
by Gonzalez, Juan, M.S., California State University, Long Beach, 2018, 76; 10978237
Abstract (Summary)

In today’s space industry, satellite formation flying has become a cost-efficient alternative solution for science, on-orbit repair and military time-critical missions. While in orbit, the satellites are exposed to the space environment and unpredictable spacecraft on-board disturbances that negatively affect the attitude control system’s ability to reduce relative position and velocity error. Satellites utilizing a PID or adaptive controller are typically tune to reduce the error induced by space environment disturbances. However, in the case of an unforeseen spacecraft disturbance, such as a fault in an IMU, the PID based attitude control system effectiveness will deteriorate and will not be able to reduce the error to an acceptable magnitude.

In order to address the shortcomings a PID-Extended Memory RNN (EMRNN) adaptive controller is proposed. A PID-EMRNN with a short memory of multiple time steps is capable of producing a control input that improves the translational position and velocity error transient response compared to a PID. The results demonstrate the PID-EMRNN controller ability to generate a faster settling and rise time for control signal curves. The PID-EMRNN also produced similar results for an altitude range of 400 km to 1000 km and inclination range of 40 to 65 degrees angles of inclination. The proposed PID-EMRNN adaptive controller has demonstrated the capability of yielding a faster position error and control signal transient response in satellite formation flying scenario.

Indexing (document details)
Advisor: Shankar, Praveen
Commitee: Bailey, Justin, Kalman, Joseph
School: California State University, Long Beach
Department: Mechanical and Aerospace Engineering
School Location: United States -- California
Source: MAI 58/05M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Engineering, Aerospace engineering
Keywords: Extended Memory RNN, Formation control, Neural network, PID- RNN satellite formation flying controller, Recurrent neural network, Satellite formation control
Publication Number: 10978237
ISBN: 978-1-392-07385-8
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