Neural network controller for internal combustion engine

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Discussion Overview

The discussion centers on the use of neural networks as controllers for internal combustion engines, particularly in the context of reducing emissions. Participants explore the advantages and potential applications of neural networks compared to traditional control methods, such as PID controllers.

Discussion Character

  • Debate/contested
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant seeks assistance in training neural network models for controlling an internal combustion engine.
  • Some participants question the necessity of neural networks, suggesting that standard PID controllers are sufficient for control tasks.
  • Another participant proposes that neural networks could optimize PID gains, indicating a potential hybrid approach.
  • There is a discussion about how modern cars utilize self-tuning PID controllers, with references to their ability to adapt based on logged operating parameters.
  • One participant expresses skepticism about the explanation of PID controllers, arguing that it does not adequately address how the gains are determined or adjusted in real-time.
  • A later reply critiques the initial post for being too vague and open-ended, suggesting that more specific questions would facilitate better assistance.
  • Several questions are posed regarding the use of neural networks in the auto industry and the limitations of conventional control theory.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the effectiveness or necessity of neural networks compared to traditional control methods. Multiple competing views remain regarding the advantages and implementation of these technologies.

Contextual Notes

Participants express uncertainty about the specifics of how self-tuning PID controllers operate and the conditions under which conventional control theory may fail. There is also a lack of clarity on the current use of neural networks in the automotive industry.

Who May Find This Useful

This discussion may be of interest to those involved in automotive engineering, control systems, and machine learning, particularly in the context of emissions reduction and advanced control strategies.

Jaco
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I'm currently doing a project on neural network controller for an internal combustion engine to reduce emissions level. Can anyone assist me in training such models (neural networks)
 
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What's the advantage of using a neural network in this situation? It seems to me a standard PID loop is perfectly sufficient...
 
Mech_Engineer said:
What's the advantage of using a neural network in this situation? It seems to me a standard PID loop is perfectly sufficient...

Perhaps a neural network can be trained to optimize PID gains.
 
Most halfway decent PID controllers are self-tuing. Your car already does this.
 
russ_watters said:
Most halfway decent PID controllers are self-tuing. Your car already does this.

Cars are self-tuning, how do they do this?
 
Cyrus said:
Cars are self-tuning, how do they do this?
For starters, by keeping track of operating parameters and matching the next one to the previously logged parameters. How much further it goes beyond that, I'm not sure, but a home thermostat does similar things. It keeps track of overshoot and adjusts the timings to compensate.
Digital thermostats with PID controller
More expensive models have a built-in PID controller, so that the thermostat knows ahead how the system will react to its commands. For instance, setting it up that temperature in the morning at 7am should be 21 degrees, makes sure that at that time the temperature will be 21 degrees (a conventional thermostat would just start working at that time). The PID controller decides at what time the system should be activated in order to reach the desired temperature at the desired time. It knows this by remembering the past behavior of the room, and the current temperature of the room.

It also makes sure that the temperature is very stable (for instance, by reducing overshoots at the end of the heating cycle) so that the comfort level is increased.
http://en.wikipedia.org/wiki/Programmable_thermostat

I'm not sure exactly how much a car's computer does, but if you've ever run the battery completely dead on your car, you may notice that it has trouble idling for a while until it figures out how to properly control the fuel/air mixture.
 
Mmmm...I don't believe this explanation. A PID controller has a set of gains which are determined based on a linear model of the system.

All you have described is how the PID controller works, not how the controller figures out which gains to use. Perhaps they are doing gain scheduling where the map a series of gains based on the closest conditions of the system at the time, but none of these explanations talk about how it (if at all) 'figures out' what gains to use.
 
I hope you guys do know that you aren't helping the OP one iota. And you haven't rolled out the welcome mat, either! Bad form!

That said, this is not a good opening post:
Jaco said:
I'm currently doing a project on neural network controller for an internal combustion engine to reduce emissions level. Can anyone assist me in training such models (neural networks)
So, first off, rolling out the official welcome mat: Welcome to PhysicsForums, Jaco!

Now I'm going to pick on you a bit, Jaco. Don't take it too personally. Hopefully this will help other newbies learn to phrase things a bit better (and you too, Jaco; this can be a very helpful place).

We do have some written rules here; you violated an unwritten one. A very big one, too. The rule is don't ask us to write a book. That is precisely what you have done by asking us "Can anyone assist me in training such models (neural networks)". The question is too open ended, too vague. The answer would require us to write a book on neural nets. There are in fact many books whose main topic is this very question.

So, Jaco, if you are still around, I'll help you in terms of questions. First question, very important:

What research have you done on this topic? We are a lot more willing to help students who have shown some work of their own.

Now some more questions that you should investigate during your research:
  • Why would someone want to use neural nets when conventional control theory does a bang-up job for the most part?
  • Where does conventional control theory start to unravel?
  • Are neural nets already in use in the auto industry? By whom?
 

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