Quote:
Originally Posted by tStano
Okay! Thank you so much, guys. I really appreciate all the effort that clearly went into those posts, and I hope it can help many students to come. Definitely got me interested in a more softwarey path in school again.
NotInControl, your post really gives a good overview without me having to worry so much about the math I don't understand. That was super helpful, I much better understand what I'm doing now.
Austin, rereading your post after NotInControl's really gave me some awesome insight. It really gives a good overview of the involved complexity in designing a system. I'll look into some math, and I'm sure that will help my learning process with all this controls stuff.
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Not a problem. Hope it helps.
Quote:
Originally Posted by tStano
So, a few more questions, which require an oversimplified explanation of my understanding:
1.You make a mathematical model of your system using all relevant states. It needs to be linear and time invariant or the math is even harder.
2. You use this to test your code which uses the variables you defined to achieve an output. You tune constants here. This is where I have questions. do you create some kind of testing code? Is hardware(sensors, even mock-ups of robot parts with similar moments?) involved?
3.Then you go to the real robot, and hopefully, its close enough to your model, and everything is peachy.
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I am going to tweak your list a little bit but first I want you to understand why LTI is so important. The field of control systems is pretty new. It wasn’t until the 1940’s did major break thoughts in control algorithms start to appear.
The reason LTI models were so important was because back then, they didn’t have dedicated computers to crunch all the numbers and calculate all of the system of equations. The first control systems were mechanical devices, like the first governors for car engines, they didn’t rely on circuits at all. Keeping the dynamics linear and the number of equations low meant people could solve them by hand or use simple calculators. This is why the number of studies and tools done in control systems to date were done based on LTI models, that’s all the majority could work with. Very complicated systems were broken down using multiple simple LTI models.
Today there is a new branch of control systems called modern control systems which venture out into designing more robust, sophisticated controllers because we have new technology at our disposal and computers which can calculate very complicated equations for us. It just turns out that the people in the 40’s to now were very smart, and the tools and principles they developed for LTI systems, can still be used today with high confidence for most applications.
Today we have computers which can solve differential equations for us. And software applications to help us design control systems. Just like CAD people have solidworks to create models, control guys have a software package called Matlab/Simulink which one of its many specialties can be used to design control systems with an add-on called the control system toolbox. This tool box allows you to enter the state space model of your plant, graphs its behavior, add simulated control systems, test then out, and design the controller virtually, before you settle on a design. You use the simulation world to test/tune your controller before you enter the software world and program it.
Once you are happy with the controller in the virtual space, you can do one of two things, you can extract your final control parameters from the model, convert it to difference equations, and code up that controller to then drive your physical system. Or you can use another toolbox in matlab to generate the code for you.. but this may lead to more trouble than good if you are not completely comfortable with auto-generated code. A majority of industries use auto-generated code from Matlab or other software packages and run them to belive it or not. I am old school in this fashion and I like to hand-write all of my own control algorithms.
Today we use these tools to develop richer models of plants. For example, for my PhD I am working on an autonomous car controller. The plant model for this system and all of its dynamics is 16 equations that need to be solved simultaneously. The controllers in Airplanes used to automatically take off, land, and fly are orders higher. These models are highly non-linear, and without the help of these software packages and calculators, attempting to hand calculate them would be too difficult for anyone to try.
Using these software tools today allow us to be better control engineers.
There are many different ways you can approach the steps which you design a control system.
What I like my students on my team to do is create a model of the plant and use Matlab/Simulink to test the plant and develop a controller for the system. They use matlab to test/tune the system until I am happy with the response.
After we are done, we take the parameters we settled on in the simulation world and use those parameters to code a software implementation of the controller in java or c++ for example. We run that on our robot and then slowly tweak the parameters in code to further get the “perfect” results.
Those steps above are where control engineers differ. Some like to use tools like Matlab, some like to do hand calculations, some like to write their own calculators. There is a whole field of different ways you can approach control system design. It appears Austin and his team have designed a lot of cool tools to help them achieve the controls they want for their robot and have a very well established programming and control model. The below is a high level view of how I approach control system design for robotics:
1. Model the System dynamics by hand. As you stated before. (I write out the equations)
2. Re-create the model in Matlab/Simulink. Use matlab/Simulink to design a controller and test the overall system in a virtual world. This requires no hardware at all, just the software, your knowledge of control systems, and the dynamics of your system.
3. Program the control system to live on the RIO. I first test the controller code written independently so that I know the algorithm works as intended. (In the end it is just a function or a class which takes in an input and spits out an output used to drive motors or something). For example I test to make sure the controller code outputs what it is supposed to when I give it a particular known input. Once I get the bugs out, I move to the next step.
4. Test that the sensors on the robot are working and give me the signals I expect and that I am assuming the proper units in my control algorithms.
5. Run the controller code on the robot, and allow it to control the hardware and keep my hand near the disable button incase things get out of control
This step has to be done carefully, because you are trusting your algorithm, and if its written incorrectly, you can start to break things on the bot.
6. Assuming things in step 5 are fine, tweak the gains ever so slightly in the code to get a faster response, reduce overshoot, etc. and make the overall performance better.
7. Done! Sit back and marvel at your creation! Try and win some banners, or bring home some control awards!
Hope this helps, feel free to ask any other questions,
Kevin