Iterative minimization of control performance criteria

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. A limiting factor in the application of these methods is the lack of useful conditions guaranteeing convergence to the global minimum; several adaptive control algorithms suffer from the same limitation. In this paper the H 2 performance criterion is analyzed in order to characterize and enlarge the set of initial parameter values from which a gradient descent algorithm can converge to its global minimum.