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Found 9 papers, 1 papers with code
Reduced-order Koopman modeling and predictive control of nonlinear processes
In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator.
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems.
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator
Koopman operator theory linearly describes nonlinear dynamical systems in high-dimensional functional space, this facilitates the application of linear control methods to nonlinear systems.
Reinforcement Learning for Control with Probabilistic Stability Guarantee
Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable.
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.
$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee
In this paper, we introduce and extend the idea of robust stability and $H_\infty$ control to design policies with both stability and robustness guarantee.
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout
In this paper, we combine variational learning and constrained reinforcement learning to simultaneously learn a Conditional Representation Model (CRM) to encode the states into safe and unsafe distributions respectively as well as to learn the corresponding safe policy.
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee
Reinforcement learning (RL) offers a principled way to achieve the optimal cumulative performance index in discrete-time nonlinear stochastic systems, which are modeled as Markov decision processes.
