I want to convert the PI controller in this instance to a PID controller, so can my network be changed to something like this?

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嘻嘻
嘻嘻 2023-10-18
评论： 轩 2024-1-4
Respected all,
The following example shows the PI controller design using TD3 reinforcement learning algorithm. I want to convert the PI controller in this instance to a PID controller, so can my actornetwork be changed to something like the following?
actorNetwork = [
featureInputLayer(numObservations3,'Normalization','none','Name','state')
fullyConnectedPILayer(randi([-50,50],1,3), 'Action')];
Tune PI Controller using Reinforcement Learning - MATLAB & Simulink - MathWorks India
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轩
轩 2024-1-4
Hello, I am trying the same method in the current time, can you leave a contact information for conmunication ?
你好，可以留一个邮箱或微信交流吗？

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采纳的回答

Emmanouil Tzorakoleftherakis
That should work - you are essentially adding one more weight to the PI layer for the D term. Potential issues may arise when you provide the error derivative as an observation (may be hard to approximate due to the trial and error nature of RL) but give it a try
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嘻嘻
嘻嘻 2023-10-23
I am very grateful for your answer, which is very helpful to me. I would like to ask you another question, I would like to know how the initial value of the network weight in the example is determined, and what impact will it have on the result? Thank you again!
Emmanouil Tzorakoleftherakis
Emmanouil Tzorakoleftherakis 2023-10-23
It looks like it's just a couple of values close to zero. RL solves an optimization problem under the hood so the better the initial guess, the more likely to get a better solution

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