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Statistical Identification and Control
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achieve performance actual process additional experiments algorithm analysis and design Appendix approach assume assumption candidate controllers Central Composite Design central member Chapter clearing solution closed loop performance confidence interval consider controller design controller parameter covariance matrix denoted Design of Experiments deterministic robustness EWMA parameter experimental data experimental design F-ratio Gaussian high probability HPD region identification and control identification model identification quality identification set input-output data inputs Lemma low probability measure of robustness measures of identification methodology misclassification event missile autopilot example model parameter vector Monte Carlo method necessarily good predictors optimal performance set boundary performance specification plant models posterior distribution posterior probability postposterior Pr(P preposterior HPD ellipse probabilistic methods probability density function probability of performance probability of stability Proposition robust control Roughly speaking run-to-run control scalar measures sensor shown in Figure standard STATIC statistical control analysis statistical identification system identification undermodeling variance worst-case