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.

Meeting Name

IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000


Mechanical and Aerospace Engineering

Keywords and Phrases

Lyapunov Method; Lyapunov Methods; Adaptive Critic Method; Missile Guidance; Neural Network; Neurocontrollers; Optimal Control; Robust Control; Stability; Uncertain Systems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2000 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2000