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Found 9 papers, 2 papers with code
Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network
Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs.
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning
Our proposed method is applied to the PID tuning of a second-order system to verify its effectiveness in improving the sample efficiency and discovering the optimal PID parameters compared to traditional TD3.
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes
The advantage of this method is that both local and global patterns in images can be captured by a simple model architecture instead of establishing deep CNN models.
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features
This paper proposes a novel local-global CNN (LG-CNN) architecture that directly accounts for both local and global features for fault diagnosis.
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation.
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning
Different from existing studies on using RL for PID tuning, in this work, we consider the closed-loop stability throughout the RL-based tuning process.
MPC Controller Tuning using Bayesian Optimization Techniques
The approach is motivated by the observation that evaluating the closed-loop performance of MPC by trial-and-error is time-consuming (e. g., every closed-loop simulation can involve solving thousands of optimization problems).
Image-Based Model Predictive Control via Dynamic Mode Decomposition
We show that the dynamics of this high-dimensional space can be accurately predicted by using a 40-dimensional DMD model and we show that the field can be manipulated satisfactorily by using an MPC controller that embeds the low-dimensional DMD model.
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems.
