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How do you mean with "better result"? For a non-linear system, machine learning can perform much better and solve more complex tasks. Like walking for a humano

How do you mean with "better result"? For a non-linear system, machine learning can perform much better and solve more complex tasks. Like walking for a humanoid robot. A linearzied PID solution can not beat that. For linear systems: Depends of what you mean with better I guess. My understanding is that you can place some of the poles of a system with a PID pretty much where you want. So for a simple linear system: no. There exist also other controllers where a LQR can be used in linear systems to get a perfect theoritical controller based upon to minimize the cost of control outputs. (yes, several outputs). However maybe a ML can be used to choose and tune the controllers better than humans? EDIT: Also, the discretication is not always perfect (backward or forward euler is common estimates). So maybe ML can help in that too?

tnecniv 9y ago
> For a non-linear system, machine learning can perform much better and solve more complex tasks. Like walking for a humanoid robot. A linearzied PID solution can not beat that. Right but you are comparing a nonlinear regression model with a linear control scheme, which isn't really fair. Plenty of nonlinear control strategies exist and are used in practice for things like humanoid walking. Moreover, many people prefer these controllers due to the theoretical guarantees they provide over a learned control policy. That said, ML does have a place in control theory, and you can find papers going back to at last the 90s (I never looked earlier but they probably exist) that combine the two, and not just in a reinforcement learning context. > However maybe a ML can be used to choose and tune the controllers better than humans? Probably not, assuming you are discussing linear systems. You would need to come up with some cost function for your ML algorithm to determine the performance of a particular control policy, but then why not just use that as the objective function for your optimal control algorithm?

tnecniv 9y ago

> For a non-linear system, machine learning can perform much better and solve more complex tasks. Like walking for a humanoid robot. A linearzied PID solution can not beat that. Right but you are comparing a nonlinear regression model with a linear control scheme, which isn't really fair. Plenty of nonlinear control strategies exist and are used in practice for things like humanoid walking. Moreover, many people prefer these controllers due to the theoretical guarantees they provide over a learned control policy. That said, ML does have a place in control theory, and you can find papers going back to at last the 90s (I never looked earlier but they probably exist) that combine the two, and not just in a reinforcement learning context. > However maybe a ML can be used to choose and tune the controllers better than humans? Probably not, assuming you are discussing linear systems. You would need to come up with some cost function for your ML algorithm to determine the performance of a particular control policy, but then why not just use that as the objective function for your optimal control algorithm?