This project proposes a new data-driven method labeled Correlation-based Tuning (CbT). The underlying idea is inspired by the well-known correlation approach in system identiﬁcation. The controller parameters are tuned iteratively either to decorrelate the closed-loop output error between designed and achieved closed-loop systems with the external reference signal (decorrelation procedure) or to reduce this correlation (correlation reduction). Ideally, the resulting closed-loop output error contains only the contribution of the noise and perfect model-following can be achieved. By the very nature of the control design criterion, the controller parameters are asymptotically insensitive to noise.
An extension of this method for the tuning of linear time-invariant multivariable controllers
is proposed for both procedures. CbT allows tuning some of the elements of the controller
transfer function matrix to satisfy the desired closed-loop performance, while the other ele-
ments are tuned to mutually decouple the closed-loop outputs.
The CbT algorithm has been tested on numerous simulation examples and implemented
experimentally on a magnetic suspension system and the active suspension system bench-
mark problem proposed for a special issue of European Journal of Control on the design and
optimization of restricted-complexity controllers.
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