On Optimal Input Design for Feed-forward Control
For control engineers, this work provides a method to design input signals that optimize feed-forward controller performance, though the results are incremental and domain-specific.
This paper addresses optimal input design for system identification when the model is used for feed-forward control. It presents a framework to design minimum-power excitation signals that ensure the resulting feed-forward controller meets output variance specifications with a given probability, and demonstrates the approach on a temperature control problem.
This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feed-forward control is presented and numerically evaluated on a temperature control problem.