Postgraduate Diploma in State Observers and Robustness
Online 800-hour Postgraduate Diploma in State Observers and Robustness. 8 modules covering IEC 61508, ISO 26262 and MATLAB with Control System Toolbox. Assigned tutor and open enrolment.
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
Analyse the controllability and observability of systems in state space.
Design full-order and reduced-order deterministic observers.
Apply linear and nonlinear Kalman filters.
Evaluate and improve the robustness of estimators against uncertainties.
Design robust controllers based on LQG and H-infinity observers.
Implement observers on digital platforms.
Validate estimators with experimental data.
Who it's for
- Control engineer who wants to specialise in State Observers and Robustness.
- Estimation and navigation engineer who wants to specialise in State Observers and Robustness.
- Battery management systems developer who wants to specialise in State Observers and Robustness.
- Embedded systems engineer who wants to specialise in State Observers and Robustness.
- Researcher in robust control who wants to specialise in State Observers and Robustness.
- Virtual sensor engineer who wants to specialise in State Observers and Robustness.
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- IEC 61508
- ISO 26262
- IEC 61131-3
- IEC 62660-1
- IEEE 754
- ISO 13849-1
8 modules · 800 h
1.1Continuous and discrete models1.2Controllability and observability1.3Controllable and observable canonical forms1.4State feedback and pole placement1.5Detectability and stabilisability1.6Models with disturbances and noise1.7Model reduction1.8Identification of state-space models1.9Time response of state-space systems1.10Practice: modelling a mass-spring-damper system
2.1Full-order Luenberger observer2.2Reduced-order observers2.3Separation principle2.4Observers with unknown inputs2.5Disturbance observers2.6High-gain observers2.7Sliding mode observers2.8Observers for nonlinear systems2.9Observer gain design by pole placement2.10Laboratory: speed observer on a motor
3.1Stochastic processes and covariance3.2Discrete Kalman filter3.3Extended Kalman filter3.4Unscented Kalman filter3.5Particle filters3.6Tuning of Q and R covariances3.7State estimation in nonlinear systems3.8Estimator consistency3.9Rauch-Tung-Striebel smoothers3.10Workshop: extended Kalman filter for navigation
4.1Modelling parametric uncertainty4.2H-infinity filter for robust estimation4.3Robust observers with LMIs4.4Sensitivity to model errors4.5Estimation with missing data4.6Estimation with delays4.7Outlier detection4.8Constrained estimation4.9Joint state and parameter estimation4.10Practice: Kalman versus H-infinity comparison
5.1Linear quadratic Gaussian (LQG) control5.2Loss of robustness in LQG5.3Loop transfer recovery (LTR)5.4H-infinity controller synthesis5.5Mu analysis and singular values5.6Multivariable stability margins5.7Robust control with LMIs5.8Controller order reduction5.9Robust tracking control with integral action5.10Laboratory: LQG/LTR design in MATLAB
6.1State-of-charge estimation in batteries6.2Inertial navigation and GNSS fusion6.3Flux estimation in sensorless motors6.4Observers in chemical processes6.5Vehicle state estimation6.6Estimation in structural monitoring6.7Virtual sensors in industry6.8Distributed estimation in networks6.9Position estimation for mobile robots6.10Case study: state-of-charge estimation for a battery
7.1Discretisation and fixed-point arithmetic7.2Implementation on microcontrollers7.3Computational complexity of filters7.4Implementation with stable factorisations7.5Automatic code generation7.6Validation with experimental data7.7HIL testing7.8Documentation and verification7.9Implementation on real-time operating systems7.10Case study: fixed-point Kalman filter
8.1Plant modelling8.2Observability analysis8.3Observer design8.4Robust controller design8.5Simulation with uncertainties8.6Digital implementation of the observer8.7Experimental validation of the estimator8.8Analysis of results8.9Report and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the enrolment period, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB with Control System Toolbox, Simulink, Python with FilterPy, YALMIP, dSPACE, in free, academic or evaluation versions. Tests are studied using real procedures for battery state-of-charge estimation, inertial and GNSS fusion in vehicles, and robust control of multivariable systems. Assessment is continuous, module by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Analyse the controllability and observability of systems in state space.
Design full-order and reduced-order deterministic observers.
Apply linear and nonlinear Kalman filters.
Evaluate and improve the robustness of estimators against uncertainties.
Design robust controllers based on LQG and H-infinity observers.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical standards documentation.
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 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You will work with MATLAB with Control System Toolbox, Simulink, Python with FilterPy, YALMIP, dSPACE. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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