Robust Adaptive Critic Based Neurocontrollers for Systems with Input Uncertainties
This document has been relocated to http://scholarsmine.mst.edu/mec_aereng_facwork/3469
There were 9 downloads as of 28 Jun 2016.
Abstract
A two-neural network approach to solving optimal control problems is described in this study. This approach called the adaptive critic method consists of two neural networks: one is called the supervisor or critic, and the other is called an action network or controller. The inputs to both these networks are the current states of the system to be controlled. Each network is trained through an output of the other network and the conditions for optimal control. When their outputs are mutually consistent, the controller network output is optimal. The optimality is limited to the underlying model. Hence, we develop a Lyapunov based theory for robust stability of these controllers when there is input uncertainty. We illustrate this approach through a longitudinal autopilot of a nonlinear missile problem.
