Master Thesis Approximating Model Predictive Controllers Using Imitation Learning
Master Thesis Approximating Model Predictive Controllers Using Imitation Learning
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Job Description
Approximate model predictive control (AMPC) has emerged as an approach to tackle the computational burden of MPC aiming to approximate the MPC policy with a computationally cheaper surrogate such as neural networks. So far the standard approach to obtaining such a surrogate policy has been based on naive behavioral cloning. This approach however has significant drawbacks resulting in the surrogate policy potentially failing to provide the original MPC guarantees. To tackle this a tailored AMPC imitation learning (IL) procedure was developed recently enabling consistent learning of a surrogate policy and ensuring that the learned policy maintains the original MPC safety and stability guarantees. This development allows for MPC-based control functions in safety-critical industrial settings.
- The goal of your thesis is to extend the statistical properties of the proposed IL procedure by analyzing the rate at which the learned policy converges to the MPC policy ultimately aiming to provide sample bounds on the error between the policies.
- Moreover the thesis could cover the investigation of more general error estimations stopping criteria and studies on sample efficiency.
- Last but not least the thesis will also focus on the deployment of the developed AMPC IL procedure to a real-world automated driving problem including a comparison with other existing approaches.
Qualifications :
- Education: Master studies in the field of Cybernetics Engineering Mathematics Computer Science or comparable
- Experience and Knowledge: profound knowledge of Machine Learning and control engineering; experience in Python DL frameworks like PyTorch TensorFlow or Jax
- Personality and Working Practice: you are an autonomous systematic working person with analytical thinking
- Languages: very good in English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Felix Berkel (Functional Department)
49 1
Elias Milios (Functional Department)
49
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
