Reinforcement Learning Based Optimal Tracking Control Under Unmeasurable Disturbances With Application to HVAC Systems

This paper presents the design of an optimal controller for solving tracking problems subject to unmeasurable disturbances and unknown system dynamics using reinforcement learning (RL). Many existing RL control methods take disturbance into account by directly measuring it and manipulating it for ex...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-12, Vol.33 (12), p.7523-7533
Hauptverfasser:	Rizvi, Syed Ali Asad, Pertzborn, Amanda J., Lin, Zongli
Format:	Artikel
Sprache:	eng
Schlagworte:	Air conditioning Algorithms and air conditioning (HVAC) control Bias Conditioning (learning) Control methods Control systems Control systems design Controllers Convergence Cost function Disturbance Disturbances Estimates Feedback Heating Heating systems HVAC HVAC equipment Learning Machine learning Mathematical model Optimal control optimal tracking Output feedback Parameters Q-learning Reinforcement reinforcement learning (RL) State feedback Steady-state System dynamics Temperature measurement Tracking control Tracking errors ventilating
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