Research OCAI-Lab

Optimal Control and Artificial Intelligence Lab

The Optimal Control and Artificial Intelligence Lab (OCAI-Lab) at Oakland University was established in 2020. The primary research of OCAI-Lab are in the areas of AI-enhanced advanced control and optimization algorithm development with application in automotive and energy systems. The projects involve fundamental research on control theory, optimization, machine learning, and statistics to support cyber-physical systems development including smart energy and transportation systems.

Research Areas

Model Predictive Control (Collaborators: Xiangyu Meng)

Event-triggered MPC (ACC22, CCTA21)
MPC with Reinforcement Learning Supervisor (ACC22)
AI-enhanced MPC Design and V&V (IJMIC20, TASE15)

Artificial Intelligence (Collaborators: Zhaojian Li, Shengrong Bu)

Reinforcement Learning (ACC22)
Generative Adversarial Networks
Scenario Generation (ISIE21, GT21-1, EGY17)

Automotive Systems (Collaborators: Lei Zhang)

Autonomous Vehicle Path Following (ACC22, CCTA21, WCX22)
Electric Vehicles Driveline Control (GT21-1)
Active Battery Cell Balancing (CDC21)
Co-simulation and Small-scale Experiment Platforms

Energy Systems (Collaborators: Guojiang Xiong, Shengrong Bu)

Control of Power Converter (ECC22, VPPC2022)
Hybrid Renewable Energy Systems (GT21-2, ISIE21, EGY17, AE17)

Project Sponsors

MSGC IFT

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in our research are those of the authors and do not necessarily reflect the views of the project sponsors.