How do you tune your control system parameters with data?

1 Data-driven tuning methods

Data-driven tuning methods are techniques that use experimental or simulated data to adjust the parameters of your control system. Unlike analytical methods, which rely on mathematical models and assumptions, data-driven methods can account for uncertainties, nonlinearities, and disturbances that affect your system. Data-driven tuning methods can be divided into two categories: direct and indirect. Direct methods use data to directly optimize the parameters of your controller, such as by minimizing a cost function or maximizing a performance index. Indirect methods use data to first identify a model of your system, and then use analytical methods to tune the controller based on the model.

2 Choosing the right data

The quality and quantity of your data can have a major impact on the success of your tuning process. Therefore, it is important to choose the right data for your system, considering factors such as the type of input signal, the number of data points, and the quality of data. Different types of input signals can be used to excite your system, such as step, ramp, sinusoidal, or random. Additionally, you need enough data points to capture the essential features of your system while avoiding too many points that may increase computational cost and overfitting. Lastly, you need to make sure your data is accurate, reliable, and representative of your system. Any sources of error should be avoided or corrected, such as measurement noise, sensor drift, or external disturbances.

3 Applying common tuning algorithms

Once you have your data, you can apply different tuning algorithms to find the optimal parameters for your controller. Trial and error is the simplest and most intuitive method, but it can be time-consuming and inefficient for complex systems. Ziegler-Nichols is a classic method for tuning PID controllers, but it may not be optimal for systems with high-order dynamics or nonlinearities. Optimization is a more advanced method that can offer better performance and flexibility than other methods, though it may require more computational resources and a good initial guess. All of these methods can help you achieve the desired response in different ways.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?