Memory neural network algorithm for missile control

Memory neural networks exhibit promising results for use as adaptive controllers for systems involving nonlinear time-delayed dynamics. By associating a memory neuron to each network neuron, we alleviate the requirement for storing and recurrently feeding a nonlinear plant´s past histories in order to adaptively control the system. Past attempts at designing a missile controller with a memory neural network produced encouraging results for the estimation facet of system modeling. This paper presents the design and simulation of a model reference adaptive controller for a missile system using memory neural networks