Launch Vehicles are governed by a complex set of nonlinear and highly coupled differential equations. In general, these equations are linearized about an equilibrium point and a linear controller is designed based upon these linearized dynamics. The linear controller is then applied to the actual system which is, of course, the original nonlinear system. This, in turn, leads to tracking errors and poor performance when the vehicle experiences significant deviation from the equilibrium condition. Also, the linear controller has difficulty handling unknown variations in the system parameters which also give rise to poor performance when applied to the actual vehicle.
A new type of neural network known as a Variable Memory Recurrent Neural Network (VMRNN) has been designed that, when used in conjunction with a linear PID controller, offers improved transient response characteristics when encountering uncertainty in the dynamic model and external disturbances.
|School:||California State University, Long Beach|
|Department:||Mechanical and Aerospace Engineering|
|School Location:||United States -- California|
|Source:||MAI 52/06M(E), Masters Abstracts International|
|Keywords:||Attitude control, Launch vehicle, Neural network, Recurrent neural networks|
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