Adaptive Critic Based Neural Networks for Control-Constrained Agile Missile Control
This document has been relocated to http://scholarsmine.mst.edu/mec_aereng_facwork/3346
There were 18 downloads as of 27 Jun 2016.
Abstract
We investigate the use of an `adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. We use neural networks with a two-network structure called `adaptive critic' to carry out the optimization process. This structure obtains an optimal controller through solving Hamiltonian equations. This approach needs no external training; each network along with the optimality equations generates the output for the other network. When the outputs are mutually consistent, the controller output is optimal. Though the networks are trained off-line, the resulting control is a feedback control
