NEWRAL NETWORK CONTROL FOR RE-ENTRY VEHICLE DURING TAEM PHASE

Document Type : Original Article

Authors

1 Graduate Student, Dpt. of Aerospace, Pusan National Univ., Busan, KOREA.

2 Associate professor, Dpt. of Aerospace, Pusan National Univ., Busan, KOREA.

3 Professor, Dpt. of Aerospace, Pusan National Univ., Busan, KOREA.

Abstract

This paper describes a result of the guidance and control for re-entry vehicle during TAEM phase. TAEM phase (Terminal Aerial Energy Management phase) has many conditions, such as density, velocity, and so on. Under these conditions, we have optimized trajectory and other states for guidance in TAEM phase. Optimized states consist of 7 variables, down range, cross range, altitude, velocity, flight path angle, vehicle’s azimuth and flight range. For optimizing, we dealt with DIDO as a programming tool, and we used feedback linearization with neural network for control re-entry vehicle. By back propagation algorithm, vehicle dynamics is approximated to real. New command can be decided using the approximated dynamics, delayed command input and plant output, control system. Using this control law, we tracked 7 states, made by optimization.

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