25 citations · 40 across the 11 of their papers we have counts for
9 papers · 1 filter
Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network
Benben Jiang, Yixing Wang, Zhenghua Ma +1
Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inap…
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning
Myisha A. Chowdhury, Qiugang Lu
Proportional-integral-derivative (PID) controllers have been widely used in the process industry. However, the satisfactory control performance of a PID controller depends strongly…
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes
Qiugang Lu, Saif S. S. Al-Wahaibi
Convolutional neural network (CNN) models have been widely used for fault diagnosis of complex systems. However, traditional CNN models rely on small kernel filters to obtain local…
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
Yun Li, Yixiu Wang, Yifu Chen +5
This paper proposes a deep learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), r…
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning
Ayub I. Lakhani, Myisha A. Chowdhury, Qiugang Lu
PID control has been the dominant control strategy in the process industry due to its simplicity in design and effectiveness in controlling a wide range of processes. However, trad…
MPC Controller Tuning using Bayesian Optimization Techniques
Qiugang Lu, Ranjeet Kumar, Victor M. Zavala
We present a Bayesian optimization (BO) framework for tuning model predictive controllers (MPC) of central heating, ventilation, and air conditioning (HVAC) plants. This approach t…
Image-Based Model Predictive Control via Dynamic Mode Decomposition
Qiugang Lu, Victor M. Zavala
We present a data-driven model predictive control (MPC) framework for systems with high state-space dimensionalities. This work is motivated by the need to exploit sensor data that…
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Qiugang Lu, Sungho Shin, Victor M. Zavala
Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates…
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
Sungho Shin, Qiugang Lu, Victor M. Zavala
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems. We observe that SID seeks to solve an…