Machine Learning is all about Data Analytics, complex Mathematical Models and Algorithms, used for Predictive Analytics. In general, Machine Learning, is all about intelligent algorithms implemented in a computer, either locally or in the cloud, so-called Cloud Computing. These algorithms work with large amounts of data ("Big Data") in order to make intelligent decisions.
In this Assignment you will use more traditional and well known “Machine Learning” principles such as System Identification, State Estimation with Kalman Filter and Model Predictive Control, and see how these techniques can be seen in the wider concept of Machine Learning.
Do you have Questions? - Go to the Question and Answers (Q&A). Very often someone else is wondering about the same as you - or perhaps someone else has experienced the same thing and found a solution for the problem? Need help outside normal office hours? Perhaps a fellow student can help you if you ask your questions here?
The Video below gives an introduction to this Lab Assignment (Please see the Lab Assignment for all the tiny details not covered in the Video):
Note! Make sure to open the Lab Assignment for the latest and most updated information.
See more Videos (YouTube Playlist) regarding the Machine Learning in Automation Systems Topics.
Below you find useful Resources (Topics, Software, Hardware and Additional Resources) for solving the Assignment. Feel free to use them the way it suits you. Even if the contents below is well structured, sometime you may find it useful to use the Google search engine like this:
"MPC site:https://www.halvorsen.blog" - just replace "MPC" with the topic you want to search for.
The following topics are covered in this assignment:
Click on these topics above and you will find lots of background information, training material, videos, and other resoures that may help you solve the assignments.
You need the following software:
The following hardware are available in the Laboratory:
Here are some other resources that may be useful when solving the assignment:
Other Kalman Filter resources:
General Videos about Kalman Filter from MathWorks:
The following Tutorials both describe the basic theory together with many practical examples (more or less complete solutions to this assignment. These are basic examples, so the code structure may be improved. Feel free to use them the way you want):
Do you want to improve your LabVIEW Application? You have been using LabVIEW in many courses and assignments, but your code structure can be improved. Your programs mainly consists of a while loop with lots of code inside. The next step is to improve the code structure by using the State Machine principles.
You should also try to use some of the features that makes your LabVIEW program better, such as Local/Gloabal variables, Property Nodes, Arrays, String Manipulation, Shift Registers, Error Handling, SubVIs.
LabVIEW Videos (YouTube)
To improve your LabVIEW Application, you can use programming techniques such as the State Machine principles, Event handling Local/Gloabal variables, Property Nodes, Arrays, String Manipulation, Shift Registers, Error Handling, SubVIs, etc.
Here you can download LabVIEW examples where these programming techniques are used. Download one of the examples and use it as a template for your own application (remove the parts you don't need and add the functionality you want using the example as a foundation for your work).
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Very often someone else is wondering about the same as you - or perhaps someone else has experienced the same thing and found a solution for the problem? Need help outside normal office hours? Perhaps a fellow student can help you if you ask your questions here?