Pitch control of an aircraft with aggregated reinforcement learning algorithms

Pitch control is a basic function of an Automatic Flight Control System (AFCS). Due to the complexity of problems, stochastic behavior, and the disturbing of the environment, traditional techniques, such as, linear feedback control, quantitative feedback theory, and adaptive control, which are all based on the explicit aerodynamic model of an aircraft, are not efficient in designing pitch controllers. This paper adopts multiple Reinforcement Learning (RL) algorithms and Cerebellar Model Articulation Controller (CMAC) techniques to design a pitch controller. In order to improve learning and control performances, a learn system named "Aggregated Multiple Reinforcement Learning System (AMRLS)" is proposed, which combines the outcomes of individual RL algorithms by using several aggregation methods. The goal of this paper is to demonstrate that the improved RL based control technology can be applied effectively to pitch control problem.