Internship Model based / Data Driven Control Strategies For Vehicle Motion Control

This internship involves developing an integrated motion control system for multi-actuated vehicles. You will design a high-level optimal controller, such as MPC, combined with a control allocation layer to coordinate actuators like brakes and steering. The role includes modeling vehicle dynamics, handling constraints, ensuring real-time feasibility, and validating performance in simulation using MATLAB/Simulink and Python to improve stability, robustness, and overall vehicle performance. 

Toyota is one of the world’s largest automobile manufacturers and a leading global company, founded in 1937. Today, Toyota sells vehicles in 170 countries and employs over 350,000 people worldwide.At Toyota Motor Europe (TME), with our headquarters based in Brussels, Belgium, around 2,700 colleagues from more than 60 nationalities work together across Europe. We are responsible for the wholesale marketing of Toyota and Lexus vehicles, parts and accessories, and we support Toyota’s European R&D, manufacturing, and engineering operations.We are on an exciting journey as we continue to evolve into a mobility company. Our goal is to create even more value and happiness for our customers through a wide range of mobility solutions.

The internship will be developed in TME R&D, Chassis group. Chassis engineering division is responsible for the:

We are inspired by the Toyota Way precepts, and we challenge to provide final customers with best quality products with the continuous improvement mindset.       

Objective

The objective of this project is to develop an integrated vehicle motion control framework for a multi-actuated ground vehicle, based on optimal control and control allocation techniques, with explicit consideration of real-time implement-ability.

Scope and Contributions

The work aims to:

• Investigate and critically review the state-of-the-art in vehicle motion control, including optimal control (e.g., MPC, MPPI), control allocation strategies, and integrated chassis control for multi-actuated systems
• Define a coherent control architecture combining to prioritize a high-level optimal controller for vehicle dynamics regulation but also lower-level actuator coordination and allocation layer. Motion control architecture is to be compatible with both trajectory and manual drive inputs interfaces.
• Formulate control strategies that explicitly account for:
• coupled vehicle dynamics (lateral, longitudinal, and roll)
• actuator constraints and redundancy
• stability and adherence limits
• Address real-time feasibility, including:
• model simplification and reduction strategies
• computational complexity analysis
• solver selection and timing considerations
• data driven approaches to control robustness and modelling
• Implement and validate the proposed framework in a high-fidelity simulation environment (potentially both offline and with DiLs), assessing:
• stability and performance improvements
• robustness to varying conditions (e.g., friction, manoeuvres)
• computational performance and real-time suitability

• Validate concept over multiple use cases (comfort, emergency, handling…) with necessary weight-scheduling through automations and pipelines to be developed.

Expected Outcome

The internship is expected to deliver a system-level control framework demonstrating effective coordination of multiple actuators through optimal control and allocation, while ensuring practical feasibility for real-time automotive implementation.

• You are fluent in English (TME’s business language)
• You are student in last year of a Master’s degree in Mechatronics, Control Systems or related domain.
• You have interest in Control Engineering, Vehicle Dynamics and Autonomous Vehicle systems.
• You have strong knowledge in Control Theory and Robust Control techniques as well in Dynamic and Vehicle Dynamics simulations and related tools (Matlab/Simulink, CarMaker and similar)
• You have strong coding skills (Python, Matlab) and highly knowledgeable in Data Science and Machine Learning.
• Knowledge on CAN protocol and CAN simulation with virtual prototyping (dSPACE) might be an asset.
• You have good communication skills and can work in autonomous way within a multicultural team

Starting date

•  1 September 2026

Duration

•  6 months

Confidentiality

Due to business requirement, not all performed projects can be reflected in the internship report. This issue needs to be discussed with candidate/school in advance.

At Toyota Motor Europe, we are committed to providing equal employment opportunities for everyone. Our recruitment decisions are based on everyone’s skills, experience, and the requirements of the role.We welcome people of all backgrounds, including different nationalities, sexual orientations, gender identities or expressions, ages, religions, ethnicities, abilities, and other personal characteristics. We do not tolerate discrimination or harassment. We believe that our individual experiences and diverse perspectives are a key strength, helping us learn from one another and grow together.Together, we can make a positive impact and go beyond the ordinary.If you need any adjustments or specific support during the recruitment or interview process, for any reason, we encourage you to let your recruiter know. We are happy to help create a process that works for you.