Control for Connected and Automated Vehicles in Mixed Traffic Environments

In this project, we develop intelligent motion planning and control methods for connected and automated vehicles in different traffic scenarios with the presence of human-driven vehicles. The foundation of these control methods lies at the intersection of optimal or predictive control, optimization, machine learning, and game theory.

Publications

• V.-A. Le and A. A. Malikopoulos, “Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization”, in 2023 American Control Conference (ACC), accepted. [arXiv preprint | website]

• V.-A. Le and A. A. Malikopoulos, “A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation”, in 2022 IEEE Conference on Decision and Control (CDC), IEEE, 2022, pp.6272-6277. [paper | website | video]

• A. M. I. Mahbub, V.-A. Le and A. A. Malikopoulos, “Safety-Prioritized Receding Horizon Control Framework for Platoon Formation in a Mixed Traffic Environment”, Automatica, vol. 155, p. 111115, 2023. [paper]

• A. M. I. Mahbub, V.-A. Le and A. A. Malikopoulos, “Safety-Aware and Data-Driven Predictive Control for Connected Automated Vehicles at a Mixed Traffic Signalized Intersection”, in 10th IFAC International Symposium on Advances in Automotive Control, IFAC, 2022, pp. 51–56. [paper]

Past projects

Learning-based Model Predictive Control with Gaussian Processes

This project aims to enhance the performance of model predictive control for single dynamical systems or multi-agent systems by using Gaussian processes to learn system dynamics.

Publications

• V.-A. Le and T. X. Nghiem, “Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes”, in 2021 IEEE Conference on Decision and Control (CDC), IEEE, 2021, pp. 2226–2231. [paper | video]

• V.-A. Le and T. X. Nghiem, “A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes”, in 2021 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2021, pp. 453–458. [paper]

• V.-A. Le and T. X. Nghiem, “Gaussian Process Based Distributed Model Predictive Control for Multi-agent Systems using Sequential Convex Programming and ADMM”, in 2020 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2020, pp. 31–36. [paper]

Adaptive Sampling for Mobile Robotic Sensor Networks

In collaboration with Dr. Linh Nguyen (Australia Federation University), we develop efficient adaptive sampling strategies for mobile robotic sensor networks for monitoring a spatial phenomenon by using Gaussian process.

Publications

• V.-A. Le, L. Nguyen, and T. X. Nghiem, “Multistep Predictions for Adaptive Sampling in Mobile Robotic Sensor Networks Using Proximal ADMM”, IEEE Access. [paper]

• V.-A. Le, L. Nguyen, and T. X. Nghiem, “ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks”, IEEE Sensors Journal, vol. 21, no. 13, pp. 15369-15378, 2021. [paper]

• V.-A. Le, L. Nguyen, and T. X. Nghiem, “An Efficient Adaptive Sampling Approach for Mobile Robotic Sensor Networks using Proximal ADMM”, in 2021 American Control Conference (ACC), IEEE, 2021, pp. 1101–1106. [paper]

Modeling and Control of Overhead Cranes/Ship-mounted Cranes

This research and experiment were conducted when I was an undergraduate research intern at School of Mechanical Engineering, Vietnam Maritime University. My duty includes designing anti-swing control algorithms for uncertain overhead cranes/ship-mounted cranes using adaptive sliding mode control approach and implementing them on NI-MyRIO microcontroller.

Publications

• V.-A. Le, X. H. Le, L. Nguyen, and X. M. Phan, “An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes”, International Journal of Automation and Computing, vol. 16, no. 5, pp. 614–627, 2019. [paper | videos]

• V.-A. Le, X. H. Le, D. T. Vu, V. T. Pham, A. T. Le, and M. C. Hoang, “Designing an adaptive controller for 3D overhead cranes using hierarchical sliding mode and neural network”, in 2018 International Conference on System Science and Engineering (ICSSE), IEEE, 2018, pp. 1–6. [paper]

• A. T. Le, M. C. Hoang, V. T. Pham, C. N. Luong, D. T. Vu, and V.-A. Le, “Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash”, Mechanical Systems and Signal Processing, vol. 112, pp. 233–250, 2018. [paper]