Controller parameter tuning using actor-critic algorithm

In this paper a new reinforcement learning strategy is used for on-line tuning the control system of the aerodynamic missile. Aerodynamics missile automatic control system's mission is to overcome the missile's flight various disturbances encountered in the process of precise and real-time...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 610; no. 1; pp. 12054 - 12061
Main Author Ahmed, Ayman Elshabrawy M
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2019
Subjects
Algorithms
Automatic control systems
Controllers
Gain schedule
Machine learning
Missile control
Missiles
Parameters
Proportional integral derivative
Reinforcement learning
Schedules
Tuning
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Summary:In this paper a new reinforcement learning strategy is used for on-line tuning the control system of the aerodynamic missile. Aerodynamics missile automatic control system's mission is to overcome the missile's flight various disturbances encountered in the process of precise and real-time control of missiles attitude. Reinforcement learning algorithm (RL) is used to tune a PID controller to replace "gain schedule" Technique usually used. The result shows that RL with the new reward function is able to optimize the PID parameters with advantage over old method in terms of convergence speed and smaller overshoot.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/610/1/012054