What is the best method for optimizing sensor placement for robustness?

  • Thread starter hermano
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So you're designing a measurement set-up and you need to position two sensors at a specific angle (alpha, beta) relative to the x-direction. However, there is only one "optimal" combination of angles in terms of robustness. To determine this optimal combination, you want to conduct a sensitivity analysis to see how each possible combination of angles will be affected by a small angular deviation. You have looked into different numerical methods to use for this, such as Monte Carlo, perturbation analyses, and Taguchi, but you are not experienced in optimization and are unsure which method is most appropriate for your problem. You are seeking suggestions and examples to help you with this. In summary, you are designing a measurement set-up and are looking for the
  • #1
hermano
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Hi,

For a measurement set-up I'm designing, I have to position two sensors which have to be placed at an angle (alpha, beta) wrt the x-direction (zero degrees). However, there is only one 'optimal' combination of angles (alpha-beta) in words of robustness. In order to determine this 'optimum', I want to do a sensitivity analysis how each possible combination of angles is sensitive to a small (angular) deviation i.e. in practice it is impossible to locate the two sensors perfect at the desired location. The combination of angles that is less sensitive to this variation of location is the optimal location I'm searching.

I searched on the internet which numerical method to use for this, however I'm not really experienced in optimization and stuff like that. I found methods like Monte Carlo, perturbation analyses, Taguchi etc. I'm reading already a several days but still I'm not sure which method to use for my problem. Anybody some suggestions which method is most appropriate? Examples (Matlab), tutorials etc.?

Any help or suggestion is welcome!

Thanks
 
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  • #2
You haven't clearly described any mathematical problem. You didn't explain what quantity or quantities are to be optimized.
 

What is a robust optimization problem?

A robust optimization problem is a mathematical optimization problem that takes into account uncertainties or variations in the input parameters. It aims to find a solution that is not only optimal under the given conditions, but also performs well under various scenarios or possible changes in the input parameters.

What are the main objectives of robust optimization?

The main objectives of robust optimization include finding a solution that is robust to uncertainties, minimizing the worst-case performance, and balancing trade-offs between robustness and optimality.

What are some common applications of robust optimization?

Robust optimization has applications in various fields such as engineering, economics, finance, and transportation. It can be used in designing robust systems, making robust decisions under uncertainties, and optimizing resource allocation in the presence of uncertainties.

What are the key differences between robust optimization and traditional optimization?

The key differences between robust optimization and traditional optimization lie in the consideration of uncertainties and the definition of optimality. Robust optimization takes into account the worst-case scenario or variations in the input parameters, while traditional optimization assumes a fixed set of parameters. Additionally, robust optimization aims to find a solution that performs well under various scenarios, rather than just optimizing for a single scenario.

What are some common methods used for solving robust optimization problems?

Some common methods for solving robust optimization problems include robust convex optimization, robust dynamic programming, and robust stochastic programming. These methods use different approaches to handle uncertainties and find robust solutions.

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