Neural networks and adaptive nonlinear control of agile antiair missiles

Research has shown that neural networks can be used to improve on approximate dynamic inversion for control of uncertain nonlinear systems. In one architecture, the neural network adaptively cancels inversion errors through on-line learning. such learning is accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring the stability of the closed-loop system. This methodology is reviewed and extended to incorporate an important class of neural networks with one sigmoidal hidden layer. An agile antiair-missile autopilot is subsequently designed using this control scheme. A control law based on approximate inversion of the nonlinear dynamics is presented. This control system is augmented by the addition of a neural network with on-line learning. Numerical results from a nonlinear agile antiair-missile simulation demonstrate the effectiveness of the resulting autopilot.