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Master'sAutonomous, ADAS & Software-Defined Vehicle Applications open

Master’s in Optimal Control and MPC Engineering for Autonomous Vehicles — Constraints, Robustness and Real Time

Online master's programme of 1,900 hours in optimal control and MPC engineering for autonomous vehicles, structured in 12 modules covering optimal control, vehicle models for MPC, linear and non-linear MPC, constraints and safety, robust and stochastic MPC, real-time embedded implementation, integrated vehicle control, state estimation, learning-based control and validation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Formulate optimal control problems for vehicles.

02

Develop vehicle models suited to MPC.

03

Design linear and non-linear MPC controllers.

04

Incorporate safety and robustness constraints.

05

Implement MPC in real time on embedded platforms.

06

Combine machine learning with predictive control.

07

Develop an MPC project for an autonomous vehicle.

Profile

Who it's for

  • Control engineers.
  • Vehicle dynamics engineers.
  • Embedded software engineers.
  • Autonomous driving engineers.
  • Optimal control researchers.
  • Validation engineers.
  • Engineering or science graduates looking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 26262 (functional safety of road vehicles)
  • ISO 21448 (safety of the intended functionality, SOTIF)
  • ISO 8855 (vehicle dynamics coordinate systems and vocabulary)
  • ISO 3888-2 (severe lane-change manoeuvre test track)
  • ISO 7401 (lateral transient response test methods)
  • AUTOSAR Classic Platform (automotive ECU software architecture)
  • MISRA C:2012 (guidelines for C in critical systems)
  • IEEE 2846 (assumptions for models in automated driving safety)
  • MATLAB/Simulink
  • acados
  • CasADi
  • IPG CarMaker
  • dSPACE
Syllabus

12 modules · 1900 h

  1. 1.1Formulation of the optimal control problem
  2. 1.2Pontryagin's maximum principle
  3. 1.3Dynamic programming and Bellman
  4. 1.4Linear quadratic regulator (LQR)
  5. 1.5LQG control with estimation
  6. 1.6Lyapunov stability
  7. 1.7Controllability and observability
  8. 1.8Discretisation of systems
  9. 1.9Discrete-time optimal control
  10. 1.10Lab: lateral LQR in MATLAB
  1. 2.1Kinematic bicycle model
  2. 2.2Dynamic model with linear tyre
  3. 2.3Non-linear tyre models
  4. 2.4Tracking error models
  5. 2.5Linearisation along the trajectory
  6. 2.6Actuator models
  7. 2.7Delays and parasitic dynamics
  8. 2.8Learned dynamics models
  9. 2.9Model validation
  10. 2.10Practical: error model for MPC
  1. 3.1Formulation of linear MPC
  2. 3.2Prediction and control horizons
  3. 3.3Quadratic cost function
  4. 3.4Input and state constraints
  5. 3.5Solving as a QP
  6. 3.6Stability and terminal cost
  7. 3.7Weight tuning
  8. 3.8Explicit MPC and critical regions
  9. 3.9Comparison with LQR
  10. 3.10Workshop: linear tracking MPC
  1. 4.1Formulation of NMPC
  2. 4.2Multiple shooting methods
  3. 4.3Real-time iteration (RTI)
  4. 4.4Sequential quadratic programming
  5. 4.5acados and CasADi tools
  6. 4.6Convergence and warm start
  7. 4.7Non-linear constraints
  8. 4.8Computational cost of the solver
  9. 4.9Applications in performance driving
  10. 4.10Lab: NMPC with acados
  1. 5.1Actuator constraints
  2. 5.2Lateral stability constraints
  3. 5.3Collision constraints
  4. 5.4Comfort constraints
  5. 5.5Soft constraints and feasibility
  6. 5.6Control invariant sets
  7. 5.7Control barrier functions
  8. 5.8Combining CBF and CLF
  9. 5.9Recovery from infeasibility
  10. 5.10Practical: MPC with a collision CBF
  1. 6.1Uncertainty in models and disturbances
  2. 6.2Tube-based robust MPC
  3. 6.3Min-max robust MPC
  4. 6.4Stochastic MPC with chance constraints
  5. 6.5Scenarios and sampling
  6. 6.6Disturbance estimation
  7. 6.7Robustness to parameter variation
  8. 6.8Online adaptation
  9. 6.9Conservativeness analysis
  10. 6.10Workshop: lateral tube-based MPC
  1. 7.1Hard real-time requirements
  2. 7.2OSQP and qpOASES embedded solvers
  3. 7.3Code generation
  4. 7.4Automotive platforms
  5. 7.5Worst-case execution time (WCET) analysis
  6. 7.6Floating-point numerical precision
  7. 7.7Solver parallelisation
  8. 7.8Integration into AUTOSAR
  9. 7.9HIL testing
  10. 7.10Lab: embedded MPC on dSPACE
  1. 8.1Coupled lateral-longitudinal control
  2. 8.2Trajectory tracking
  3. 8.3Stability control with MPC
  4. 8.4Torque vectoring distribution
  5. 8.5Control at the limits of grip
  6. 8.6Coordination with brakes
  7. 8.7Control in parking
  8. 8.8Trailer control
  9. 8.9Performance evaluation
  10. 8.10Practical: coupled MPC in CarMaker
  1. 9.1Kalman filters for control
  2. 9.2Sideslip angle estimation
  3. 9.3Tyre-road friction estimation
  4. 9.4State observers
  5. 9.5Moving horizon estimation (MHE)
  6. 9.6Fusion with vehicle sensors
  7. 9.7Sensor fault detection
  8. 9.8Real-time estimation
  9. 9.9Interaction between estimator and controller
  10. 9.10Workshop: MHE of lateral sideslip
  1. 10.1Learning-based MPC
  2. 10.2Gaussian processes in MPC
  3. 10.3Neural networks as models
  4. 10.4MPC learned by imitation
  5. 10.5Reinforcement learning and MPC
  6. 10.6Iterative learning control
  7. 10.7Safety guarantees
  8. 10.8Data and training
  9. 10.9Validation of learned controllers
  10. 10.10Lab: MPC with a Gaussian process
  1. 11.1Controller safety requirements
  2. 11.2ISO 26262 in control software
  3. 11.3Stability verification
  4. 11.4MIL, SIL and HIL testing
  5. 11.5Track testing
  6. 11.6Tracking and comfort metrics
  7. 11.7Failure mode analysis
  8. 11.8Safety monitors
  9. 11.9Validation documentation
  10. 11.10Case study: MPC validation on track
  1. 12.1Definition of the application
  2. 12.2Vehicle modelling
  3. 12.3MPC design
  4. 12.4Constraint handling and robustness
  5. 12.5Real-time implementation
  6. 12.6Integration with planning
  7. 12.7Experimental validation of the solution
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, acados, CasADi, IPG CarMaker and dSPACE, in free, academic or evaluation versions. Testing is studied through real procedures for ISO 3888 double lane-change manoeuvres, controller validation in HIL and tracking tests on track. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 26262, ISO 21448, ISO 8855, ISO 3888-2, ISO 7401, AUTOSAR Classic Platform, MISRA C:2012, IEEE 2846.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 26262, ISO 21448 and ISO 8855 applied to real cases, not as a theoretical annex.
Industry software:MATLAB/Simulink, acados, CasADi, IPG CarMaker and dSPACE, in free, academic or evaluation versions.
Master's Thesis with defence:A real-time MPC controller for an autonomous vehicle application, from vehicle modelling to experimental validation, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Application and vehicle model
Define the application and model the vehicle for MPC.
MPC design
Design the linear or non-linear MPC controller.
Constraints and robustness
Handle constraints, safety and robustness, for example with CBFs or tubes.
Real-time implementation
Implement the controller in real time and integrate it with planning.
Master's Thesis
Validate the solution experimentally and present conclusions, defended before a panel.
Career opportunities

Where this takes you

Control EngineerVehicle Dynamics EngineerEmbedded Software EngineerAutonomous Driving EngineerOptimal Control ResearcherValidation Engineer
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB/Simulink, acados, CasADi, IPG CarMaker and dSPACE, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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