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Master'sRobotics, Automation & Control Applications open

Master’s in Advanced Control and Dynamics Engineering

Online master's programme of 1,900 hours in advanced control and dynamics engineering, structured in 12 modules covering system modelling and identification, state-space control, robust, predictive and adaptive control, multibody dynamics, motion control and real-time digital implementation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

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Learning outcomes

What you will be able to do

01

Model and identify linear and nonlinear dynamic systems.

02

Analyse stability, controllability and observability.

03

Design state feedback controllers and observers.

04

Apply robust and predictive control techniques.

05

Model the dynamics of multibody systems.

06

Implement digital controllers in real time.

07

Evaluate the performance of advanced controllers.

Profile

Who it's for

  • Control engineers moving beyond PID towards modern control methods.
  • Dynamic systems engineers.
  • Motion control specialists working with servo drives and multi-axis machines.
  • Mechatronics R&D engineers.
  • Embedded control software developers.
  • Simulation engineers.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • IEC 61131-3:2013 (programming languages for programmable controllers)
  • IEC 61800-7-1:2015 (generic interface and profiles for power drive systems)
  • IEC 61508-3:2010 (functional safety: software requirements)
  • IEC 60050-351:2013 (international electrotechnical vocabulary: control technology)
  • ISO 26262-6:2018 (road vehicle functional safety: software level)
  • IEC 61131-6:2012 (functional safety for programmable controllers)
  • MATLAB/Simulink
  • Simscape Multibody
  • CasADi
  • Python Control
  • dSPACE
Syllabus

12 modules · 1900 h

  1. 1.1Linear state equations
  2. 1.2Linearisation around equilibria
  3. 1.3Euler-Lagrange models
  4. 1.4Mechanical, electrical and thermal systems
  5. 1.5Discretisation and sampling
  6. 1.6Parametric model identification
  7. 1.7Identification with PRBS signals
  8. 1.8Validation of identified models
  9. 1.9Parametric model uncertainty
  10. 1.10Lab: identification of a servomotor
  1. 2.1Linear Lyapunov stability
  2. 2.2Controllability and observability
  3. 2.3Canonical forms and realisations
  4. 2.4Eigenvalues and modes
  5. 2.5Kalman decomposition
  6. 2.6Time response and transition matrix
  7. 2.7Coupled MIMO systems
  8. 2.8Transmission zeros
  9. 2.9Model reduction by balanced truncation
  10. 2.10Practical: analysis of a MIMO system
  1. 3.1Pole placement
  2. 3.2Design of Luenberger observers
  3. 3.3Separation principle
  4. 3.4Integral action in state space
  5. 3.5LQR optimal control
  6. 3.6Choice of weighting matrices
  7. 3.7Kalman filter and LQG
  8. 3.8Robustness of LQG
  9. 3.9Digital implementation of the controller
  10. 3.10Lab: LQR control of an inverted pendulum
  1. 4.1Structured and unstructured uncertainty
  2. 4.2Gain and phase margins
  3. 4.3Sensitivity functions
  4. 4.4H-infinity control synthesis
  5. 4.5McFarlane-Glover loop shaping
  6. 4.6Mu analysis and structured singular value
  7. 4.7Synthesis of robust controllers
  8. 4.8Controller order reduction
  9. 4.9Design trade-offs
  10. 4.10Practical: H-infinity design in MATLAB
  1. 5.1Formulation of the MPC problem
  2. 5.2Prediction and control horizons
  3. 5.3Constraints on inputs and states
  4. 5.4Online quadratic programming
  5. 5.5Stability and terminal sets
  6. 5.6Explicit MPC and critical regions
  7. 5.7State estimation for MPC
  8. 5.8Tuning weights and horizons
  9. 5.9Real-time solvers
  10. 5.10Workshop: MPC of a thermal system
  1. 6.1Model reference adaptive control
  2. 6.2Self-tuning regulators with identification
  3. 6.3Recursive least-squares identification
  4. 6.4Stability of adaptive schemes
  5. 6.5Gain-scheduled control
  6. 6.6Linear parameter-varying systems
  7. 6.7Robustness of adaptation
  8. 6.8Applications in time-varying processes
  9. 6.9Comparison with robust control
  10. 6.10Practical: MRAC control in simulation
  1. 7.1Rigid body dynamics
  2. 7.2Newton-Euler formulation
  3. 7.3Constraints and Lagrange multipliers
  4. 7.4Simulation with Simscape Multibody
  5. 7.5Vibrations in lightweight structures
  6. 7.6Flexible modes in mechanisms
  7. 7.7Friction and backlash
  8. 7.8Dynamics of robot manipulators
  9. 7.9Numerical integration and stiffness
  10. 7.10Lab: multibody model of a mechanism
  1. 8.1Servo drives and cascaded loops
  2. 8.2Current, speed and position control
  3. 8.3Acceleration and friction feedforward
  4. 8.4Motion profile generation
  5. 8.5Electronic cam control
  6. 8.6Axis synchronisation
  7. 8.7Vibration suppression with input shaping
  8. 8.8Tuning industrial servos
  9. 8.9Control of linear motors
  10. 8.10Practical: tuning cascaded loops
  1. 9.1Feedback linearisation
  2. 9.2Sliding mode control
  3. 9.3Recursive backstepping design
  4. 9.4Control Lyapunov functions
  5. 9.5Saturation and anti-windup
  6. 9.6Systems with dead zones
  7. 9.7Control of underactuated systems
  8. 9.8Comparison with linear control
  9. 9.9Examples in robotics
  10. 9.10Case study: control of a crane with a suspended load
  1. 10.1Sampling theorem and aliasing
  2. 10.2Direct discrete design
  3. 10.3Computation delays and jitter
  4. 10.4Quantisation and numerical precision
  5. 10.5Implementation on microcontrollers
  6. 10.6Automatic code generation
  7. 10.7Real-time operating systems
  8. 10.8Validation with hardware-in-the-loop
  9. 10.9Diagnosis of loops in the plant
  10. 10.10Lab: discrete control on a microcontroller
  1. 11.1Control of vehicles and drones
  2. 11.2Control of chemical processes
  3. 11.3Control of power converters
  4. 11.4Control of mobile robots
  5. 11.5Control of aerospace systems
  6. 11.6Control of machine tools
  7. 11.7Control of wind turbines
  8. 11.8Control of biomedical systems
  9. 11.9Trends: data-driven control
  10. 11.10Case study: quadcopter control
  1. 12.1Selection of the system to be controlled
  2. 12.2Modelling and identification
  3. 12.3Dynamic analysis of the system
  4. 12.4Design of alternative controllers
  5. 12.5Simulation and comparison
  6. 12.6Real-time implementation
  7. 12.7Evaluation of performance and robustness
  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

100% online training 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 each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, Simscape Multibody, CasADi, Python Control and dSPACE, in free, academic or evaluation versions. Tests are studied on real procedures: identification and control of servo systems, design of robust controllers in simulation and hardware-in-the-loop validation. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEC 61131-3:2013, IEC 61800-7-1:2015, IEC 61508-3:2010, IEC 60050-351:2013, ISO 26262-6:2018, IEC 61131-6:2012.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEC 61131-3:2013, IEC 61800-7-1:2015 and IEC 61508-3:2010 applied to real cases, not as a theoretical annex.
Industry software:MATLAB/Simulink, Simscape Multibody, CasADi, Python Control and dSPACE, in free, academic or evaluation versions.
Master's Thesis with defence:the modelling, design, comparison and real-time implementation of advanced controllers for a real dynamic system, 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.

System identification
Model and identify the dynamics of a real system such as a servomotor.
State feedback and LQR
Design state feedback controllers and observers, including LQR and LQG.
Robust and predictive design
Design and compare H-infinity and MPC controllers in simulation.
Real-time implementation
Implement the discrete controller and validate it with hardware-in-the-loop.
Master's Thesis
A complete advanced control solution evaluated for performance and robustness, defended before a panel.
Career opportunities

Where this takes you

Control engineerDynamic systems engineerMotion control specialistMechatronics R&D engineerEmbedded control software developerSimulation 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 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.

Apply now Scholarships
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, Simscape Multibody, CasADi, Python Control 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, plus 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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