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Found 17 papers, 0 papers with code
Data-Based System Representation and Synchronization for Multiagent Systems
We consider the cases of homogeneous and heterogeneous systems.
Data-Based Control of Continuous-Time Linear Systems with Performance Specifications
First, we formulate and solve a trajectory-reference control problem, on which desired closed-loop trajectories are known and a controller that allows the system to closely follow those trajectories is computed.
Gaussian Process-Based Nonlinear Moving Horizon Estimation
On the other hand, we exploit the posterior variances of the Gaussian processes to design the weighting matrices in the MHE cost function and account for the uncertainty in the learned system dynamics.
Sample-based nonlinear detectability for discrete-time systems
This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems.
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.
Robust Stability of Gaussian Process Based Moving Horizon Estimation
In this paper, we introduce a Gaussian process based moving horizon estimation (MHE) framework.
An Efficient Off-Policy Reinforcement Learning Algorithm for the Continuous-Time LQR Problem
Moreover, we formulate the policy evaluation step as the solution of a Sylvester-transpose equation, which increases the efficiency of its solution.
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.
Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems
In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced.
Sample-based observability of linear discrete-time systems
In this work, sample-based observability of linear discrete-time systems is studied.
On a Continuous-Time Version of Willems' Lemma
In this paper, a method to represent every input-output trajectory of a continuous-time linear system in terms of previously collected data is presented.
Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems
This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time 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.
