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 algorithm - no process model is used. Two topics are important regarding this algorithm: the convergence rate and the convergence to the global minimum. This paper discusses these issues and provides a method for choosing the step size to ensure convergence with high convergence rate, as well as a test to verify at each step whether or not the algorithm is converging to the global minimum.