Non-iterative data-driven controller tuning with guaranteed stability: Application to direct-drive pick-and-place robot

This paper illustrates the practical application of non-iterative correlation-based tuning with guaranteed stability. In this method, a sufficient condition for closed-loop stability is defined as the H_∞-norm of a particular error function. This norm is then estimated using data from one closed-loop experiment. The method is applied to a pick-and-place robot. It is shown that the proposed constraints for stability are effective without being overly conservative. Furthermore, it is shown how the method can be used to systematically design low-order controllers.