Accession Number : ADA318751

Title :   Nonlinear Adaptive Control of Agile Anti-Air Missiles Using Neural Networks,

Corporate Author : GEORGIA INST OF TECH ATLANTA SCHOOL OFAEROSPACE ENGINEERING

Personal Author(s) : McFarland, Michael B. ; Calise, Anthony J.

PDF Url : ADA318751

Report Date : 1996

Pagination or Media Count : 9

Abstract : Research has shown that neural networks can be used to improve upon approximate dynamic inversion controllers in the case of uncertain nonlinear systems. In one possible architecture, the neural network adaptively cancels linearization errors through on-line learning. Learning may be accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring the stability of the closed-loop system. In this paper, the authors discuss the evolution of this methodology and its application in a bank-to-turn autopilot design for an agile anti-air missile. Additional consideration is given to robustness of the proposed controller. First, a control scheme based on approximate inversion of the vehicle dynamics is presented. This nonlinear control system is then augmented by the addition of a feedforward neural network with on-line learning. Finally, the resulting control law is demonstrated in a nonlinear simulation and its performance is evaluated relative to a more traditional gain-scheduled linear autopilot.

Descriptors :   *NEURAL NETS, *AUTOMATIC PILOTS, *ANTIAIRCRAFT MISSILES, COMPUTERIZED SIMULATION, ADAPTIVE CONTROL SYSTEMS, COMPUTER AIDED DESIGN, LEARNING MACHINES, NONLINEAR SYSTEMS, SYSTEMS ANALYSIS, CONTROL THEORY, ONLINE SYSTEMS, ANTIAIRCRAFT DEFENSE SYSTEMS, CLOSED LOOP SYSTEMS.

Subject Categories : Cybernetics
      Guided Missiles
      Antiaircraft Defense Systems

Distribution Statement : APPROVED FOR PUBLIC RELEASE