Metrics to characterize control performance without simulation
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Hello,
I am trying to find a metric to tune the parameters of a variable structure controller but right now I am using metric computed after simulation and it is very long plus the metrics cannot be computed in some cases. Is there a metric that could be used to tune the controller without simulating the model and that could be computed in any case?
Thank you
2 Commenti
Mathieu NOE
il 17 Dic 2024
hello , do you have a simple working code we can use ?
IMO,performance is related to error signals and I don't see how you could have a somewhat equivalent info without simulating your closed loop on a given test signal
Risposte (1)
Naga
il 23 Dic 2024
Tuning controller parameters without running simulations can be tricky, but there are ways to speed up or avoid simulations entirely:
- Use simplified models, replace full simulations with analytical models or reduced-order approximations.
- Evaluate controller parameters directly using stability margins, control effort, or robustness criteria.
- Surrogate model, train a machine learning model (e.g., Gaussian Process) to approximate performance metrics based on a few simulations, then use it for optimization.
- Add constraints or rules to exclude obviously unstable or infeasible parameters before simulation.
For your PSO code, you could integrate a surrogate model. This might look like replacing your current fitness function with a trained model or pre-checking parameter stability to avoid wasting time on bad candidates.
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