Model Predictive Control (MPC) for safe control design: Application on Autonomous Vehicles (Quanser Qcar)

Type de recrutement
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Topic:  Model Predictive Control (MPC) for safe control design: Application on Autonomous Vehicles (Quanser Qcar)

Research Lab: Centre de Recherche en Automatique de Nancy,  CRAN (Research Centre of Automatic Control in Nancy), UMR 7039, Université de Lorraine (University of Lorraine), France.
Department: Control Identification and Diagnosis (CID), CRAN.

Websites: CRAN:

Scholarship Duration:  5 months
Period: April 2024 - August 2024

Mayank Shekhar JHA, Associate Professor, CRAN, University of Lorraine, France.
Didier Theilliol, Full Professor, CRAN, University of Lorraine, France.

Description: The subject seeks development as well as implementation of the existing state of the art methods for design optimal (sub-optimal) control laws for autonomous vehicles. In particular, optimal control design using state feedback approaches [1,2] will be envisaged for various purposes such as trajectory following (point to point, line following etc.), lane following, obstacle detection and avoidance, without as well as with vision (camera) based knowledge.

The internship will focus on objectives in a progressive manner starting from model-based feedback control design for car control, followed by synthesis of estimation techniques (linear quadratic Gaussian), model predictive control using the state of the art and finally a health aware control design will be targeted by incorporating the state of heath of batteries within the control design [3,4]

The algorithms will be implemented in real time over the 1/10th scaled autonomous car Quanser CAR (QCAR) studio (see information here), available at CRAN (Polytech Nancy). See Annex for more details.


In this research subject, learning of control laws using policy gradient methods including DDPG will be targeted.

The objectives at high level include:

·         Study of existing work (bibliographic survey) on state feedback control design and model predictive control (MPC) for car (4 wheel robot) control.

·         Control design for point to point, line and trajectory following and lane following.  

o    Hands on tests on QCAR.

o    Implementation of Code/program in MATLAB/Python.

·         Design of health aware control by incorporating battery degradation data within the control design.


The internship will provide possibilities for scientific publication in international conferences and reputed scientific journals.



[1] Kanso, S., Jha, M. S., & Theilliol, D. (2022, September). Degradation Tolerant Optimal Control Design for Linear Discrete-Times Systems. In International Conference on Diagnostics of Processes and Systems (pp. 398-409). Cham: Springer International Publishing.

[2] Jha, M. S., Theilliol, D., & Weber, P. (2023). Model‐free optimal tracking over finite horizon using adaptive dynamic programming. Optimal Control Applications and Methods.



[3] Khelassi, A., Theilliol, D., & Weber, P. (2011). Reconfigurability analysis for reliable fault-tolerant control design. International Journal of Applied Mathematics and Computer Science, 21(3), 431-439.


[4] F. Karimi Pour, D. Theilliol , V. Puig, G. Cembrano, Health-aware control design based on remaining useful life estimation for autonomous racing vehicle. ISA Transactions, Available online 21 April 2020





Contact: and


Dr. Mayank S JHA Maitre de Conférences (Associate Professor)  CRAN, UMR 7039, CNRS. Université de Lorraine, France. Tel: +33-(0)-660673251  Personal Webpage


Prof. Didier THEILLIOL - Université de Lorraine
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