Markov Data-Based LQG Control

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Data Informativity: A New Perspective on Data-Driven Analysis and Control (2019, methodology): “A similar approach was used in [9]–[11], [13] for the finite horizon data-driven LQR/LQG problem.”
On Lyapunov functions and data-driven dissipativity (2017, background): “In recent developments of data-based approaches to control (Shi and Skelton (2000); Safonov and Tsao (1997); Maupong and Rapisarda (2016, 2017); Markovsky and Rapisarda (2008)), where the design of a controller is based on system data rather than models, which are in many real-life situations not…”
Reinforcement Learning for Partially Observable Dynamic Processes: Adaptive Dynamic Programming Using Measured Output Data (2011, methodology): “These techniques are as follows: data-based predictive control [22], unfalsified control [27], Markov data-based linear quadratic Gaussian control [31], disturbance-based control [34], simultaneous perturbation stochastic approximation [32], pulse-response-based control [3], iterative feedback tunin”
Data-driven simulation and control (2008, methodology): “Procedures for deriving multivariable linear quadratic Gaussian controller, using the plant impulse response, are proposed in Furuta and Wongsaisuwan ( 1995 ) and Shi and Skelton ( 2000 ).”