7 papers
Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control
Xiao Li, Tianhao Wei, Changliu Liu +2
Neural networks are powerful tools for data-driven modeling of complex dynamical systems, enhancing predictive capability for control applications. However, their inherent nonlinea…
Safe Adaptive Cruise Control Under Perception Uncertainty: A Deep Ensemble and Conformal Tube Model Predictive Control Approach
Xiao Li, Anouck Girard, Ilya Kolmanovsky
Autonomous driving heavily relies on perception systems to interpret the environment for decision-making. To enhance robustness in these safety critical applications, this paper co…
Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control
Xiao Li, H. Eric Tseng, Anouck Girard +1
Autonomous driving depends on perception systems to understand the environment and to inform downstream decision-making. While advanced perception systems utilizing black-box Deep…
System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models
Xiao Li, Yutong Li, Anouck Girard +1
The Neural Network (NN), as a black-box function approximator, has been considered in many control and robotics applications. However, difficulties in verifying the overall system…
Model Predictive Control of Diesel Engine Emissions Based on Neural Network Modeling
Jiadi Zhang, Xiao Li, Ilya Kolmanovsky +2
This paper addresses the control of diesel engine nitrogen oxides (NOx) and Soot emissions through the application of Model Predictive Control (MPC). The developments described in…
Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control
Jiadi Zhang, Xiao Li, Mohammad Reza Amini +3
This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is t…