Course detail

Real Time Control and Simulation

FSI-RPO Acad. year: 2027/2028 Summer semester

Students will learn about advanced techniques of real-time simulations, identification, advanced control systems and state/parameter estimation. Theoretical knowledge will be applied in a team project involving the complete control design for a real laboratory model.

Language of instruction

Czech

Number of ECTS credits

5

Supervisor

doc. Ing. Robert Grepl, Ph.D.

Department

Institute of Solid Mechanics, Mechatronics and Biomechanics

Entry knowledge

Knowledge of mathematics, kinematics, dynamics equal to previous studies and programming in MATLAB/Simulink.

Rules for evaluation and completion of the course

The course is concluded with a graded credit and evaluated according to the standard ECTS grading scale (0–100 points; a minimum total of 50 points is required to pass). The overall evaluation consists of two parts:

  1. Laboratory team project (max. 25 points): The project consists of solving assigned laboratory tasks throughout the course, with the completion condition being the successful implementation and submission of at least 4 out of the 7 specified tasks no later than the end of the teaching part of the course. Evaluation is based on the functionality of the proposed solution on a real model, the quality of execution, and the individual contribution of each team member verified during continuous monitoring and task defense in the laboratory sessions.
  2. Final knowledge assessment for awarding the graded credit (max. 75 points): After fulfilling the team project requirements, students take a written test followed by an individual oral discussion assessing their understanding of the course topics and practical tasks solved during the course.

Aims

The course focuses on advanced real-time simulation techniques and related software and hardware. Theoretical knowledge will be applied in laboratory exercises where students will learn the process of identifying and designing advanced controls for a real laboratory model.

Upon completion of the course, students will gain knowledge and skills in the following areas:

  • Rapid prototyping of control systems and HIL (principles, software tools and hardware).
  • System identification
  • State control
  • Kalman filter
  • Nonlinear control
  • Complex team project development

The study programmes with the given course

Programme N-MET-P: Mechatronics, Master's, compulsory

Type of course unit

 

Lecture

26 hours, compulsory

Syllabus

The course is taught in a block format. Lectures, exercises, and laboratory sessions are distributed unevenly across the weeks.


1) Review of fundamentals of mechatronic systems modelling, principles of formulating equations of motion, state-space representation of continuous-time and discrete-time systems.


2) Introduction to real-time simulations in mechatronics, implementation of models in MATLAB/Simulink environment, numerical methods for real-time simulation, influence of solver and step-size selection on simulation stability.


3) System identification – methods for acquiring and pre-processing experimental data. Parameter estimation of simulation models using the least squares method (LS, RLS), fundamentals of black-box modelling, and model validation.


4) State-space control I – State-space analysis of systems (controllability, observability). State feedback design using pole placement.


5) State-space control II – State control with an integrator (setpoint tracking and disturbance rejection). Optimal state control – linear quadratic regulator (LQR).


6) State observers I – Luenberger observer, separation principle. Combining state controller with observer and its effect on stability.


7) State observers II – State estimation under measurement and process noise. Kalman filter (KF) for linear systems, Extended Kalman Filter (EKF) for non-linear systems.


8) Model Predictive Control (MPC).


9) Rapid Control Prototyping (RCP). Principles of RCP, HW and SW architecture for real-time computing, sampling, latency, and determinism. Platforms and simulation tools (e.g. dSPACE).


10) Hardware-in-the-Loop (HIL) simulation and code generation. Principles of real-time HIL testing of control units on models. Automatic C/C++ code generation from Simulink for simulators and embedded systems.


11) Selected non-linear control methods. Feedforward non-linearity compensation, friction compensation, fundamentals of feedback linearization of non-linear models.


12-13) Consultations and team project presentations

Laboratory exercise

26 hours, compulsory

Syllabus

1) Modelling of mechatronic systems – solving selected tasks


2) Simulation of selected tasks in MATLAB/Simulink environment


3) Experimental parameter identification of a laboratory model


4 – 5) State-space control – solving selected laboratory tasks


6 – 7) Design and tuning of a state observer for a laboratory model


8) MPC – solving selected tasks


9 – 12) Solving team projects – system identification, control design and testing


13) Consultations and presentation of team project solutions