Optimizing the convergence of data-based controller tuning

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H
₂
performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. The convergence to the global minimum of the performance criterion depends on the initial controller parameters and on the step size of each iteration. This paper discusses these issues and provides a method for choosing the step size to ensure convergence to the global minimum utilizing the lowest possible number of iterations.