document: Controller performance analysis with LQG benchmark obtained under closed loop conditions This paper proposes a new method for obtaining a linear quadratic Gaussian -LRB- LQG -RRB- benchmark in terms of the variances of process input and output from closed-loop data , for assessing the controller performance . LQG benchmark has been proposed in the literature to assess controller performance since the LQG tradeoff curve represents the limit of performance in terms of input and output variances . However , an explicit parametric model is required to calculate the LQG benchmark . In this work , we propose a data driven subspace approach to calculate the LQG benchmark under closed-loop conditions with certain external excitations . The optimal LQG-benchmark variances are obtained directly from the subspace matrices corresponding to the deterministic inputs and the stochastic inputs , which are identified using closed-loop data with setpoint excitation . These variances are used for assessing the controller performance . The method proposed in this paper is applicable to both univariate and multivariate systems . Profit analysis for the implementation of feedforward control to the existing feedback-only control system is also analyzed under the optimal LQG performance framework
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