Assessment and diagnosis of feedforward/feedback control system
A sequence of statistical hypothesis procedures for assessing a feedforward/feedback control performance, diagnosing and removing its fault causes is proposed. Based on the controlled output, the current variance is contributed by the feedback only effect and the combination of feedback with feedforward effect, respectively. These effect variations are systematically conducted by a sequence of the statistic hypothesis testing and the isolation strategy to compare the current control performance and the achievable benchmark operating one. A diagnosis tree with hierarchical structure is constructed. The capability of the proposal is illustrated through a simulation case with multiple faults.
Sequential Control Performance Diagnosis of Steel Processes
Abstract A sequential method for Control Performance Diagnosis using a classification tree to predict possible root-causes of poor performance is presented. The classification tree methodology is used to combine process pre-assessment (nonlinearities detection, delays estimation and controller assessment), control performance assessment (CPA) and analysis of variance (ANOVA) into an integrated framework. An initial process data set is analysed and the results are used as decision thresholds for the classification tree. The methodology is capable to identify root-causes such as poor tuning, inadequate control structure, nonlinearities, process mismatch and disturbance changes. The proposed methodology is applied to individual loops of a tandem cold rolling mill.
Identification of time delays using a polynomial identification method
Abstract An algorithm for the exact least-squares identification of an approximate continuous-time time-delay system is derived and its operation verified by simulation.