Postgraduate Diploma in MPC with Constraints and Real-Time
Online 800 h Postgraduate Diploma in MPC with Constraints and Real-Time. 8 modules covering IEC 61131-3, IEC 61508 and MATLAB Model Predictive Control 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
Formulate predictive control problems with receding horizon.
Incorporate hard and soft constraints while guaranteeing feasibility.
Select and apply numerical optimisation solvers.
Implement real-time MPC on embedded platforms.
Design robust and economic NMPC controllers.
Apply MPC to industrial processes and mechatronic systems.
Validate and commission predictive controllers.
Who it's for
- Advanced control engineer who wants to specialise in MPC with Constraints and Real-Time.
- Process engineer who wants to specialise in MPC with Constraints and Real-Time.
- Embedded control software developer who wants to specialise in MPC with Constraints and Real-Time.
- Autonomous driving systems engineer who wants to specialise in MPC with Constraints and Real-Time.
- Researcher in optimisation and control who wants to specialise in MPC with Constraints and Real-Time.
- Process control consultant who wants to specialise in MPC with Constraints and Real-Time.
- Graduates in engineering or science seeking to enter the chemical industry.
What you'll work with
- IEC 61131-3
- IEC 61508
- ISO 26262
- IEC 61499
- ISA-95
- IEC 62264-1
8 modules · 800 h
1.1Receding horizon and prediction1.2Prediction models in state space1.3Quadratic cost function1.4Formulation as a QP problem1.5Integral action and reference tracking1.6Comparison with PID and LQR1.7Industrial history: DMC and GPC1.8Tuning of horizons and weights1.9Step and impulse response models1.10Practical: first linear MPC in MATLAB
2.1Constraints on inputs and rates2.2Constraints on states and outputs2.3Soft constraints with slack variables2.4Guarantee of recursive feasibility2.5Terminal invariant sets2.6Stability with terminal cost2.7Prioritisation of constraints2.8Infeasibility and recovery strategies2.9Input rate constraints per step2.10Laboratory: MPC with constraints on a tank
3.1Convex quadratic programming3.2Active-set methods3.3Interior-point methods3.4First-order methods: ADMM3.5Condensing versus sparse formulation3.6Warm start3.7OSQP, qpOASES and HPIPM solvers3.8Numerical accuracy and conditioning3.9Computation times and solver tolerances3.10Workshop: comparison of QP solvers in Python
4.1Explicit MPC and polyhedral partitions4.2Real-time iteration (RTI)4.3Computation time limits4.4Embedded code generation with acados4.5Implementation on PLC and FPGA4.6Latencies and synchronisation with sampling4.7Worst-case execution time verification4.8MPC on microcontrollers4.9Selection of sampling period and horizon4.10Practical: embedded MPC generated with acados
5.1Nonlinear MPC formulation5.2Multiple shooting methods5.3Tube-based robust MPC5.4Stochastic MPC with chance constraints5.5MPC with state estimation5.6Adaptive MPC with updated model5.7Economic MPC with operating cost5.8Learning-based MPC5.9Moving horizon estimation (MHE)5.10Laboratory: NMPC for a vehicle with CasADi
6.1Control of distillation columns6.2Control of converters and motors6.3Autonomous driving and lane tracking6.4Building HVAC control6.5Control of manipulator robots6.6Energy management of microgrids6.7Control of polymerisation processes6.8Return on investment of advanced control6.9Trajectory control for drones6.10Case study: MPC for building air conditioning
7.1Model identification for MPC7.2Closed-loop simulation testing7.3Hardware-in-the-Loop testing of MPC7.4Controller performance monitoring7.5Model maintenance7.6Mode management and bumpless transfer7.7Operation documentation7.8Operator training7.9Detection of a degraded model in operation7.10Case study: commissioning on a pilot plant
8.1Process and model selection8.2Definition of constraints8.3Linear MPC design8.4Nonlinear or robust extension8.5Embedded controller implementation8.6HIL testing and final tuning8.7Evaluation of computation times8.8Analysis of results8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 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 intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Model Predictive Control Toolbox, CasADi, acados, OSQP, dSPACE, in free, academic or evaluation versions. Case studies cover real predictive control procedures for constrained chemical processes, embedded MPC in vehicles and robots, and building energy management with MPC. Assessment is continuous per 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.
Formulate predictive control problems with receding horizon.
Incorporate hard and soft constraints while guaranteeing feasibility.
Select and apply numerical optimisation solvers.
Implement real-time MPC on embedded platforms.
Design robust and economic NMPC controllers.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow the reading of 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 consists of 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
Work is carried out with MATLAB Model Predictive Control Toolbox, CasADi, acados, OSQP, dSPACE. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM upon passing the continuous assessment and the integrating project. It is own 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 recognition policy when you enrol.
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