Making your system behave as desired is your control system's task. No matter if your application domain is thermodynamics, mechanics or electronics, control system design is a complex and iterative process that should end with a suitable strategy, controller setting and implementation.

Iterating on a real plant is a resource intensive process that can be expensive, time-consuming and hazardous. With computer-aided engineering (CAE) you can instead do model-based design (MBD): a computer model of the physical system is developed and used in the control design iterations. This will give faster and safer iteration cycles for the engineers, reducing the use of project resources.

Applying this methodology from the beginning of your product development, you can also identify the control challenges early and adapt the product design to reduce challenge level and therefore also project risks.

Modelon has control engineering as a core area of expertise. We employ a team of experts with solid experience that can help you through the process of design and analysis of control systems, as well as modeling of your system, during the model-based design procedure.


Development of a process model

  • With an appropriate level of complexity: simpler models for control design, higher fidelity for analysis and validation
  • That is computationally efficient for real-time applications such as hardware-in-loop testing
  • That is calibrated using data from experiments or other software
  • That is based on first principles, empirical correlations or a combination of both

Model-based control design

  • Evaluation of sensor and actuator configuration
  • Assessment of the achievable control performance based on optimal control
  • Reference following or disturbance rejection
  • Design of individual control loops or of more advanced multivariable schemes

Controller analysis

  • Robustness analysis against disturbances, model errors or measurement noise
  • Impact analysis of parameter variations through design-of-experiments methods

Controller implementation

  • Digital controller design
  • Code-generation support for Simulink Coder/ dSPACE
  • Interfacing plant model executable to target platform

Validation and verification

  •  Development of plant models for MIL/SIL/HIL testing of control system
  • Configuration of test suites

Examples of control services applications

Modelica model from Thermal Power Library that is used for control structure analysis and tuning.

The model contains the economizer, evaporator and superheater, as well as several valves to be controlled for optimal heat transfer from the recovered steam. 

Modelica model of a truck and trailer, built with the Vehicle Dynamics Library, connected to a velocity controller from a cruise control system.

The system setup allows the modeler and control system engineer to analyze the control system performance before being implemented on the real system.

Modelica model from Vehicle Dynamics Library imported to Matlab/Simulink using FMI Toolbox where a control loop is  tuned and analyzed. 

With the FMI toolbox you can, among others,  linearize your Modelica process model for control system development and perform parameter sweeps for analysis of robustness and performance of your closed loop system.

P&ID diagram of an oil distillation tower with several flow, pressure and temperature control loops.

This type of diagram is used as a base when building a Modelica model of the system to be analyzed.

The analysis can be made in both Dymola directly using the Modelica model, or in MATLAB when exported as FMU and imported using the FMI Toolbox for MATLAB/Simulink

Control system design - in Simulink, versus FMI Toolbox for MATLAB/Simulink as common interface, which enables FMUs-export from Simulink and by this provides additional customer value.