Stable Iterative Correlation-based Tuning algorithm for servo systems

This paper proposes an iterative Correlation-based Tuning (CbT) algorithm which guarantees the control system stability throughout the iterations. The new algorithm is based on a Robbins-Monro procedure which ensures the iterative tuning of controller parameters such that to minimize a cost function expressed as the squared sum of the cross-correlation function between the output error and the reference input. The control system stability is tested using the coprime factor uncertainty of the controller, and the small gain theorem for discrete-time systems is applied. Nonparametric frequency domain models are employed in the calculation of the bounds on systems' gains. A case study concerning the speed control of a nonlinear servo system is included to validate the new stable CbT algorithm, and experimental results are given.