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.
Identification of stochastic time lag systems in the presence of colored noise
Akstraet--This paper is concerned with identification of stochastic time lag systems corrupted by colored noise. Correlation analysis is used for time delay estimation. Some important extensions are made via time series analysis so that the time delay can be estimated accurately even when the input to the identified plant is a common stationary signal and is correlated with the process noise. By applying a new modified least-squares estimation method to eliminating the bias in the system parameters estimates arising from colored noise, an unbiased identification algorithm for jointly estimating the time delay and system parameters can be obtained based on correlation analysis. The proposed identification schemes may be used both in batches and recursively. Numerical examples are given as well.
The Asymptotic Mean Squared Error of Multistep Prediction from the Regression Model with Autoregressive Errors
Abstract The asymptotic prediction mean squared error is derived for the regression model with autoregressive errors. The results cover the cases where future values of the exogenous variables are known, where some exogenous variables are predicted from autoregressive processes, and where lagged endogenous variables are included in the model. A result is also obtained for the predictor that ignores autocorrelation of the errors and is based on ordinary least squares (OLS) estimates of the regression parameters.