Learning optimal controllers for linear systems with multiplicative noise via policy gradient
B Gravell, PM Esfahani… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical
benchmark for reinforcement learning-based control of complex dynamical systems with …
benchmark for reinforcement learning-based control of complex dynamical systems with …
[HTML][HTML] Wireless control: Retrospective and open vistas
The convergence of wireless networks and control engineering has been a technological
driver since the beginning of this century. It has significantly contributed to a wide set of …
driver since the beginning of this century. It has significantly contributed to a wide set of …
A general framework for learning-based distributionally robust MPC of Markov jump systems
M Schuurmans, P Patrinos - IEEE Transactions on Automatic …, 2023 - ieeexplore.ieee.org
In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …
chance-constrained Markov jump systems with unknown switching probabilities. Using …
Data-driven distributionally robust LQR with multiplicative noise
P Coppens, M Schuurmans… - Learning for dynamics …, 2020 - proceedings.mlr.press
We present a data-driven method for solving the linear quadratic regulator problem for
systems with multiplicative disturbances, the distribution of which is only known through …
systems with multiplicative disturbances, the distribution of which is only known through …
Learning robust control for LQR systems with multiplicative noise via policy gradient
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical
benchmark for reinforcement learning-based control of complex dynamical systems with …
benchmark for reinforcement learning-based control of complex dynamical systems with …
Distributional robustness in minimax linear quadratic control with Wasserstein distance
To address the issue of inaccurate distributions in discrete-time stochastic systems, a
minimax linear quadratic control method using the Wasserstein metric is proposed. Our …
minimax linear quadratic control method using the Wasserstein metric is proposed. Our …
Interaction-aware model predictive control for autonomous driving
R Wang, M Schuurmans… - 2023 European Control …, 2023 - ieeexplore.ieee.org
We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …
merging tasks in automated driving. The MPC strategy is integrated with an online learning …
Risk-averse model predictive operation control of islanded microgrids
In this paper, we present a risk-averse model predictive control (MPC) scheme for the
operation of islanded microgrids with very high share of renewable energy sources. The …
operation of islanded microgrids with very high share of renewable energy sources. The …
Policy optimization for Markovian jump linear quadratic control: Gradient method and global convergence
JP Jansch-Porto, B Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recently, policy optimization has received renewed attention from the control community
due to various applications in reinforcement learning tasks. In this article, we investigate the …
due to various applications in reinforcement learning tasks. In this article, we investigate the …
Data-driven control of markov jump systems: Sample complexity and regret bounds
Learning how to effectively control unknown dynamical systems from data is crucial for
intelligent autonomous systems. This task becomes a significant challenge when the …
intelligent autonomous systems. This task becomes a significant challenge when the …