Adaptive neurocontrol design applied to the attitude control problem

A general architecture for neuro-genetic adaptive control is described and contrasted with purely neural approaches to adaptive control. The system is demonstrated on the attitude control problem for a rigid body (satellite) equipped with thrusters about each principal axis. By simulating the dynamic system and applying standard neural network techniques a locally predictive network is first trained to the prevailing dynamics. The inputs for the network are a small history of system states up to the present and a set of current control inputs, the outputs are the next system state. It is assumed that a hardware implementation of this network is used to evaluate hypothetical control inputs very rapidly. A genetic algorithm with a simple goal function then searches the space of hypothetical control inputs, whose fitness is evaluated by the neural network, so as to find a satisfactory set of control inputs before the end of the predicted time interval--the whole process is then repeated. The results indicate that such an architecture is able to master the attitude control problem for arbitrary slew angles, with arbitrary unknown dynamics, large unknown deterministic perturbing forces (which left to themselves induced chaotic motion), and noise in the sensor system.