Overview
What this challenge is about.
Implement nonlinear MPC for a delivery robot, compare against PID, and recommend the best controller. Earn a verifiable certificate.
The scenario
The startup (15 people, late-seed, around 60 active campus customers across Switzerland and Austria) has been blocked from outdoor pilots by tracking error in windy conditions.
The Brief
What you'll do, and what you'll demonstrate.
Determine whether nonlinear MPC outperforms the PID baseline enough to justify a controller swap in production.
Earning criteria — what you'll demonstrate
- Implement nonlinear MPC for a real robotics platform
- Compare classical (PID) and modern (MPC) controllers on equal footing
- Analyze controller robustness to disturbances
- Document a controller decision the chief engineer can defend
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Model Predictive Control
Apply model predictive control to solve real industry problems and demonstrate production-level capability.
- Optimal Control
Apply optimal control to solve real industry problems and demonstrate production-level capability.
- Robotics Simulation
Apply robotics simulation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Control Systems
Apply control systems to solve real industry problems and demonstrate production-level capability.
- Trajectory Tracking
Apply trajectory tracking to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Owning a controller swap with a defensible decision document is exactly the kind of project that lets a junior AI engineer earn ownership of a real robotics subsystem.
This challenge sharpens
- model-predictive-control
- control-systems
- python
Machine Learning Engineer
Implementing optimization-based controllers and benchmarking against incumbents is a direct skill transfer to MLE roles on learned-controller teams.
This challenge sharpens
- model-predictive-control
- optimal-control
- trajectory-tracking