Internship Model based

Overview

This internship involves developing an integrated motion control system for multi‑actuated ground vehicles. The role focuses on designing a high‑level optimal controller (e.g., MPC) combined with a control‑allocation layer to coordinate actuators such as brakes and steering. Responsibilities include modeling vehicle dynamics, managing constraints, ensuring real‑time feasibility, and validating performance in simulation using MATLAB/Simulink and Python to improve stability, robustness, and overall vehicle performance.

Company Information

TOYOTA is one of the world’s largest automobile manufacturers and a leading global corporation. Founded in 1937, Toyota now sells vehicles in 170 countries and employs over 350,000 people. Based in Brussels, Belgium, Toyota Motor Europe (TME) handles the wholesale marketing of Toyota and Lexus vehicles, parts & accessories, and manages Toyota’s European R&D, manufacturing, and engineering operations.

Team / Division

• Development of chassis systems and components (brake, steering, suspension, tyre, shift, pedals, …) for local production, utilising European technologies and suppliers to achieve the highest driving performance and quality standards at competitive cost.
• Vehicle dynamic performance development through simulation and testing of systems and vehicles.
• Investigation and pre‑development of new European technology for automotive chassis systems and components.
• Research and application of state‑of‑the‑art methodologies for chassis systems and chassis electronic systems’ design and validation, enabling most competitive performance and efficient development processes while securing our quality and safety standards.

Your Project

Integrated Optimal Motion Control and Allocation for Multi‑Actuated Vehicles

Objective

The objective 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 implementability.

Scope and Contributions

• Investigate and critically review the state‑of‑the‑art in vehicle motion control, including optimal control (MPC, MPPI), control‑allocation strategies, and integrated chassis control for multi‑actuated systems.
• Define a coherent control architecture that prioritises a high‑level optimal controller for vehicle dynamics regulation and a lower‑level actuator coordination and allocation layer; the architecture is compatible with both trajectory and manual‑drive 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 framework in a high‑fidelity simulation environment (potentially both offline and with DILS), assessing:
  • Stability and performance improvements
  • Robustness to varying conditions (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 should 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.

Your Profile

• Fluency in English (TME’s business language).
• Student in the final year of a Master’s degree in Mechatronics, Control Systems or a related domain.
• Interest in Control Engineering, Vehicle Dynamics and Autonomous Vehicle systems.
• Strong knowledge in Control Theory and Robust Control techniques, and experience in Dynamic and Vehicle Dynamics simulations using MATLAB/Simulink, CarMaker or similar tools.
• Strong coding skills (Python, MATLAB) and knowledge of Data Science and Machine Learning.
• Knowledge of CAN protocol and CAN simulation with virtual prototyping (dSPACE) is an asset.
• Good communication skills and ability to work autonomously within a multicultural team.

Starting Date

1 September 2026

Duration

6 months

Confidentiality Statement

Due to business requirements, not all performed projects can be reflected in the internship report. This must be discussed with the candidate/s