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Control over the network is a popular approach to control complex and distributed systems. However, delays are induced by the network, leading to the degradation in control performance. To analyze the characteristics of these delays, experiments are conducted to collect and to analyze the round trip time (RTT) between Beijing and Hong Kong. These results are presented in this paper. Large surges with...
Rapid, accurate and reliable measurements of biological oxygen demand (BOD) are a key basis for monitoring and controlling wastewater treatment processes (WWTP). A kind of soft measurement based on the dynamic neural network (DNN) is proposed in this paper, which can be used to monitor and model the important parameters of the wastewater treatment process on-line. The main parts of the soft measurement...
A major difficulty affecting the control of product quality in industrial polymerization reactors is the lack of suitable on-line polymer property measurements. In this article, a predictive model of polymer properties is deduced for industrial polyethylene process based on the kinetics of ethylene polymerization. According to the approximation error upper bound of the deduced predictive model, a...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
An alternative method of Model Predictive Control called Process Value Model Predictive Control (PV-MPC) is presented that overcomes a number of the inherent problems in both set point and controller output MPC. The PV-MPC is based on process value (PV) as a manipulated variable in place of either set point (SP) or controller output (OP). This new MPC method utilizes PV-based models that are devoid...
Computationally efficient robust model predictive control algorithm applicable for linear systems with polytopic model uncertainty as well as bounded additive disturbances is explored. A form of control scheme consisting of a static state feedback and a dynamically evolving perturbation is used in such a way that the cost function can be minimized in a receding horizon fashion over a pre-determined...
In this paper, parameter identification based on Wiener-Hammerstein models were proposed for main steam temperature in a superheater system of a thermal power plant. Two approaches of identification methods were evaluated. One based on prediction error, and the other was based on maximum likelihood method. The effectiveness was confirmed through simulation studies.
A nondestructive optical method for determining the geographic origins of rice was investigated. Average absorption spectra of rice for three different geographic origins were analyzed. Direct orthogonal signal correction (DOSC), standard normal variate transformation (SNV) combined with detrending, multiply scatter calibration (MSC) and Savitzky-Golay second-order derivative transformation (S.Golay...
The ever increasing demand and restriction on having additional new infrastructure, forces the existing power system network to work at its maximum possible limits. In order to increase the power transfer through given infrastructure, the use of FACTS devices is common and very well known. Here a Model Predictive Control based TCSC controller is used for improving the transient stability response...
A variant of model predictive control (MPC), called Multiplexed MPC (MMPC) has been proposed recently. The motivation for MMPC is to reduce real-time computational load. The reduction in computational load can be used gainfully to increase sampling rate and improve performance. In design, selecting a suitable sampling interval taking into account the computing resources available is a key consideration...
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