Hi Samira,

It's not very clear what the objective of this 'basic example' would be when it comes to analyzing a control system. The type of control system and plant under control matters as well. Having said that, here's some options:

• You can use the built-in function for random sampling. This could be placed inside Matlab function and used within the Simulink environment. Here is the link: https://www.mathworks.com/help/stats/randsample.html. This could be used to analyze the robustness (phase and gain margins) of pre-defined gain (e.g PID) or design weights (H-inf) limits.
• You could use the random number block. Here is the link: https://www.mathworks.com/help/simulink/slref/randomnumber.html. This could be superimposed with your input signals (reference error) and/or feedback signal (measurement error).

There's alot more you can do. It all depends on the intent of the exercise/problem.

Hope it helps! :)

Hello Samira,

Monte-Carlo Simulation estimates the stochastic or deterministic parameters based on Random Sampling. This simulation is the replacement of the given system under analysis by a system described by a probability distribution and then drawing random samples from probability distribution by means of random numbers. So, Monte-Carlo Simulation requires generation of random numbers.

Generation of random numbers will give you the idea of Simulation and it will help you in solving your problem.

So, go for generation of random numbers, it may help you.

Hope, it will work.

Hi Samira,

Referring to the Examples 9.3 and 9.4 in Prof. Lewis' book (Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, 2e), the attached MATLAB example (m-file) shows how to simulate a stochastic control system.

Hope this helps!

https://www.mathworks.com/support/books/book48893.html

Prof. Lewis' home page:

http://www.uta.edu/utari/acs/