Simulation of Two-Rate Neural Network Control for Stochastic Model of Missile Autopilot

摘要

This paper describes a two-rate stochastic control system as state-space (SS) type decomposed and discretized models of stochastic subsystems with the "fast" and "slow" artificial neural networks (NNs). These NNs are used as the dynamic subsystems controllers. This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate NN hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate SS decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various discretization periods and with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.