State feedback controllers are appealing due to their structural simplicity. Nevertheless, when stabilizing a given plant, dynamics of this type of controllers could lead the static feedback gain to take higher values than desired. On the other hand, a dynamic state feedback controller is capable of achieving the same or even better performance by introducing additional parameters into the model to be designed. In this document, the Linear Quadratic Tracking problem will be tackled using a (linear) dynamic state feedback controller, whose parameters will be chosen by means of applying reinforcement learning techniques, which have been proved to be especially useful when the model of the plant to be controlled is unknown or inaccurate.
Adaptive control, furuta pendulum, reinforcement learning.
Miguel A. SOLIS, Manuel OLIVARES, Héctor ALLENDE, "Stabilizing Dynamic State Feedback Controller Synthesis: A Reinforcement Learning Approach", Studies in Informatics and Control, ISSN 1220-1766, vol. 25(2), pp. 245-254, 2016. https://doi.org/10.24846/v25i2y201612
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).