Midcourse Guidance Law with Neural Networks


A dual neural network 'adaptive critic approach' is used in this study to generate midcourse guidance commands for a missile to reach a predicted impact point while maximizing its final velocity. The adaptive critic approach is based on approximate dynamic programming. The first network, called a 'critic', network, outputs the Lagrangian multipliers arising in an optimal control formulation while the second network, called an 'action' network, outputs the optimal guidance/control. While a typical adaptive critic structure consists of a single critic and a single controller, the midcourse guidance problem needs indexing in terms of the independent variable and therefore there is a cascade of critics and controllers each set for a different index. Every controller learns from the critic at the previous stage. Though the networks are trained off-line, the resulting control is in a feedback form. A midcourse guidance problem is the first testbed for this approach where the input is vector-valued. The numerical results for a number of scenarios show that the network performance is excellent. Corroboration for optimality is provided by comparisons of the numerical solutions using a shooting method for a number of scenarios. Numerical results demonstrate some attractive features of the adaptive critic approach and show that this formulation works very well in guiding the missile to its final conditions from an envelope of initial conditions. This application also demonstrates the use of adaptive critics as a tool to solve a class of 'free final time' problems in optimal control, which are usually very difficult.


Mechanical and Aerospace Engineering

Keywords and Phrases

Missile; Neural Networks; Optimal Control

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Document Type

Article - Journal

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© 2005 Professional Engineering Publishing (Institution of Mechanical Engineers), All rights reserved.

Publication Date

01 Jan 2005