MATLAB and Simulink Racing Lounge: Principles of Control Design
From the series: Improving Your Racecar Development
Christoph Hahn, Daniel Weida, MathWorks
To demonstrate the principles of control design in Simulink® and MATLAB®, Daniel Weida and Christoph Hahn, of MathWorks, show how to control a throttle. Developing a plant model in Simulink, as done with the throttle example, is a basic starting point for controlling a system. From this point, Daniel and Christoph create a robust controller capable of withstanding possible uncertainties and adjust the response to certain requirements using optimization.
In this throttle model, a PID controller (standard for linear controls) is first added to create a control loop. A signal builder block is used for flexibility, grouping of multiple signals, and simulating multiple scenarios. The simulation results can then be viewed with the Scope feature, even if there are multiple signals.
After simulating the realistic behavior of the throttle, the next step is to improve the system behavior. It can be influenced through Simulink by optimizing the controller parameters. You can tune the overall system in Simulink and see a direct comparison of performance and robustness. A workflow to overcome uncertainties can also be created to affect quality control. Uncertainties for all parameters make your simulation more realistic and Daniel uses the Robust Control Toolbox™ to show how to identify the worst case scenario. He then explores the topic of optimization and how to model a controller without violating specified restrictions. Optimization helps to define requirements in a system and is a very powerful tool in control design.
Despite a lot of mathematics, with the user-friendly tools available for control design it is very feasible. Following the Simulink control design workflows helps to create robust and stable designs. The worst-case analysis feature for uncertain systems is a great tool to mitigate problems in the future and the optimization capabilities act as a bridge between the model and the real world, ensuring that the control system behaves in a compatible manner. Further material on control design can be found in the links below.
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