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Iterative Feedback Tuning is a purely data driven tuning algorithm for optimizing control parameters based on closed loop data. The algorithm is designed to produce an unbiased estimate of the performance cost function gradient for iteratively improving the control parameters to achieve optimal loop performance. This tuning method has been developed for systems based on a transfer function representation. This paper presents a state feedback control system with a state observer and its transfer function equivalent in terms of input output dynamics. It is shown how the parameters in the closed loop state space system can be tuned by Iterative Feedback Tuning utilizing this equivalent representation. A simulation example illustrates that the tuning converges to the known analytical solution for the feedback control gain and to the Kalman gain in the state observer. In case of parametric uncertainty, different choices of tuning parameters are investigated. It is shown that the data driven tuning method produces optimal performance for convex problems when it is the model parameter estimates in the observer that are tuned

Iterative feedback tuning of uncertain state space systems