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Found 7 papers, 0 papers with code
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems.
Notes on data-driven output-feedback control of linear MIMO systems
Recent works have approached the data-driven design of output-feedback controllers for discrete-time LTI systems by constructing non-minimal state vectors composed of past inputs and outputs.
Sample- and computationally efficient data-driven predictive control
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data.
Data-based system representations from irregularly measured data
Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control.
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems.
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems
The proposed method does not require any knowledge of the system dynamics, and it enjoys significant efficiency advantages over other data-based optimal control methods in the literature.
Data-Based System Analysis and Control of Flat Nonlinear Systems
Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persistently exciting, input-output trajectory of that system.
