Abstract
The widely used minimum variance benchmark for assessing the performance of control loops is known to have limitations, such as not taking the available controller complexity into account and being indifferent to the control effort applied in regulating stochastic disturbances. In this work, a novel filter-based method is proposed to address the shortcomings of the minimum variance benchmarking and to provide a realistic performance measure for industrial proportional−integral (PI) control loops using experimental closed-loop data. The filter-based technique is used to evaluate the PI achievable performance and to generate the input−output variance trade-off curve within the PI controller domain.
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