Engineering topics
Publications and source records attributed to Ziyad, Nigel.
Abstract In this paper, an approach to increase the degree of autonomy of flight software is proposed. We describe an enhancement of the Attitude Determination and Control System by augmenting it with self-calibration capability. Conventional attitude estimation and control algorithms are combined with higher level decision making and machine learning algorithms in order to deal with the uncertainty and complexity of the problem.
In this paper, an approach to increase the degree of autonomy of flight software is proposed. We describe an enhancement of the Attitude Determination and Control System by augmenting it with self-calibration capability. Conventional attitude estimation and control algorithms are combined with higher level decision making and machine learning algorithms in order to deal with the uncertainty and complexity of the problem.
This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.