Dynamic Data Reconciliation for Enhancing Performance of Minimum Variance Control in Univariate and Multivariate Systems

2016-09-08T00:00:00Z (GMT) by Zhengjiang Zhang Junghui Chen
Healthy controllers are required in order for the control systems to maintain a high level of performance. In past research, minimum variance control (MVC) played a crucial role as a benchmark in performance monitoring because of the attractive theoretical and computational properties associated with it. Since the influence of measurement errors has not been explicitly considered in the MVC theory in stochastic control systems, this paper first analyzes the influence of measurement errors on the control performance of MVC in both univariate and multivariate systems. And then the dynamic data reconciliation (DDR) methods are proposed and combined in the procedure of MVC/multivariate MVC (MMVC) to decrease the influence of measurement errors and to enhance the control performance. Considering both random measurement errors and gross errors, the effectiveness of MVC/MMVC combined with DDR on the variances of process outputs is illustrated by two simulated cases, including both univariate and multivariate systems. An actual pilot scale experiment with two inputs and two outputs is used to demonstrate the effectiveness of MMVC combined with DDR. Results of simulation and experiment show that MVC/MMVC combined with DDR can efficiently enhance control performance.