Airship control based on Q-Learning algorithm and neural network

An autonomous on-line learning control strategy based on adaptive modeling mechanism was proposed aimed at system modeling and parameter identification problems resulting from dynamic model uncertainties in modern airship control. An adaptive method to establish airship control Markov decision process (MDP) model was introduced on the foundation of analyzing airship's actual motion. On-line learning was carried out by Q-Learning algorithm, and cerebellar model articulation controller (CMAC) network was brought in for generalization of action value functions to accelerate algorithm convergence speed. Simulations of this autonomous on-line learning controller and comparisons with parameters turned PID controllers in normal control tasks were presented to demonstrate Q-Learning controller's effectiveness. The results show that the controller's on-line learning processes can converge in a few hours and the airship control MDP model established by the adaptive method satisfies the need of normal control tasks. The controller designed in this paper obtains similar precision as PID controllers and performs even more intelligently.