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.
Master’s in Optimal Control and MPC Engineering for Autonomous Vehicles — Constraints, Robustness and Real Time — studying from Australia
Australia has active research in autonomous systems, particularly in mining, agriculture and freight, where autonomous haulage and automated equipment are well established. Universities and research bodies collaborate closely with industry on perception, sensor fusion and vehicle software. Advanced driver assistance systems are increasingly standard in imported vehicles, creating demand for engineers who understand safety validation and software integration. Employers range from mining technology firms and research institutions to global automotive software suppliers operating local engineering teams.
Fees for Australia: A$12,600 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Is this qualification recognised by Australian engineering bodies?
SEIUM's qualifications are private and not official state degrees, and they are not accredited by Engineers Australia or any other national body. Professional recognition for engineers in Australia usually runs through accredited university degrees and Engineers Australia membership. Whether a private qualification is valued is decided by employers or the relevant body.
Which Australian employers work in autonomous and ADAS software?
Australia has automotive suppliers, mining and logistics companies using autonomous systems, and technology firms working on driver assistance and vehicle software. Employers usually assess your coding ability, project portfolio and experience more than the name of a private course. Ask prospective employers how they regard the qualification.
How demanding is the course and is it workable with a job in Australia?
The programmes are online and taught in English, so you can study from home without needing a student visa. Specialist courses are 300 hours, postgraduate diplomas 800 hours and master's 1,900 hours, so consider your available time carefully. An assigned technical tutor supports you, fees are payable in instalments, and employer sponsorship may be possible.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Formulate optimal control problems for vehicles.
Develop vehicle models suited to MPC.
Design linear and non-linear MPC controllers.
Incorporate safety and robustness constraints.
Implement MPC in real time on embedded platforms.
Combine machine learning with predictive control.
Develop an MPC project for an autonomous vehicle.
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.
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
12 modules · 1900 h
1.1Formulation of the optimal control problem1.2Pontryagin's maximum principle1.3Dynamic programming and Bellman1.4Linear quadratic regulator (LQR)1.5LQG control with estimation1.6Lyapunov stability1.7Controllability and observability1.8Discretisation of systems1.9Discrete-time optimal control1.10Lab: lateral LQR in MATLAB
2.1Kinematic bicycle model2.2Dynamic model with linear tyre2.3Non-linear tyre models2.4Tracking error models2.5Linearisation along the trajectory2.6Actuator models2.7Delays and parasitic dynamics2.8Learned dynamics models2.9Model validation2.10Practical: error model for MPC
3.1Formulation of linear MPC3.2Prediction and control horizons3.3Quadratic cost function3.4Input and state constraints3.5Solving as a QP3.6Stability and terminal cost3.7Weight tuning3.8Explicit MPC and critical regions3.9Comparison with LQR3.10Workshop: linear tracking MPC
4.1Formulation of NMPC4.2Multiple shooting methods4.3Real-time iteration (RTI)4.4Sequential quadratic programming4.5acados and CasADi tools4.6Convergence and warm start4.7Non-linear constraints4.8Computational cost of the solver4.9Applications in performance driving4.10Lab: NMPC with acados
5.1Actuator constraints5.2Lateral stability constraints5.3Collision constraints5.4Comfort constraints5.5Soft constraints and feasibility5.6Control invariant sets5.7Control barrier functions5.8Combining CBF and CLF5.9Recovery from infeasibility5.10Practical: MPC with a collision CBF
6.1Uncertainty in models and disturbances6.2Tube-based robust MPC6.3Min-max robust MPC6.4Stochastic MPC with chance constraints6.5Scenarios and sampling6.6Disturbance estimation6.7Robustness to parameter variation6.8Online adaptation6.9Conservativeness analysis6.10Workshop: lateral tube-based MPC
7.1Hard real-time requirements7.2OSQP and qpOASES embedded solvers7.3Code generation7.4Automotive platforms7.5Worst-case execution time (WCET) analysis7.6Floating-point numerical precision7.7Solver parallelisation7.8Integration into AUTOSAR7.9HIL testing7.10Lab: embedded MPC on dSPACE
8.1Coupled lateral-longitudinal control8.2Trajectory tracking8.3Stability control with MPC8.4Torque vectoring distribution8.5Control at the limits of grip8.6Coordination with brakes8.7Control in parking8.8Trailer control8.9Performance evaluation8.10Practical: coupled MPC in CarMaker
9.1Kalman filters for control9.2Sideslip angle estimation9.3Tyre-road friction estimation9.4State observers9.5Moving horizon estimation (MHE)9.6Fusion with vehicle sensors9.7Sensor fault detection9.8Real-time estimation9.9Interaction between estimator and controller9.10Workshop: MHE of lateral sideslip
10.1Learning-based MPC10.2Gaussian processes in MPC10.3Neural networks as models10.4MPC learned by imitation10.5Reinforcement learning and MPC10.6Iterative learning control10.7Safety guarantees10.8Data and training10.9Validation of learned controllers10.10Lab: MPC with a Gaussian process
11.1Controller safety requirements11.2ISO 26262 in control software11.3Stability verification11.4MIL, SIL and HIL testing11.5Track testing11.6Tracking and comfort metrics11.7Failure mode analysis11.8Safety monitors11.9Validation documentation11.10Case study: MPC validation on track
12.1Definition of the application12.2Vehicle modelling12.3MPC design12.4Constraint handling and robustness12.5Real-time implementation12.6Integration with planning12.7Experimental validation of the solution12.8Conclusions and future work12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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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