Network-based Process Control Research Articles

Multi-loop networked process control: A synchronized approach

Modern day process control uses digital controllers which are based on the principle of distributed rather than centralized control. Distributing controllers, sensors and actuators across a plant entails considerable wiring which can be reduced substantially by integrating the components of a control loop over a network. The other advantages include greater flexibility and higher reliability with lower hardware redundancy. The controllers and sensors are on a network and can take over the function of a failed component automatically, without the need of manual reconfiguration, thus eliminating the need of having a redundant component for each and every component. Though elaborate techniques have been developed for Single Input Single Output (SISO) systems, the major challenge lies in extending these ideas to control a practical process plant where de-centralized control is actually achieved through control of individual SISO control loops derived through de-coupling of the original system. Multiple loops increase network load and hence the sampling times associated with the control loops and makes synchronization difficult. This paper presents a methodology by which network based process control can be applied to practical process plants, with a simple direct synchronization mechanism.

FULLY AUTOMATED TEST-PLANT FOR CALIBRATION OF FLOW-/HEAT-METERS

In autumn 2002 ARC Seibersdorf research GmbH was awarded a contract from GEW Rheinenergie Köln GmbH covering supply and erection of a heat-meter test plant. It is designed for fully automated operation and maximum flexibility so that all heat-meters on the market can be tested on a maximum uncertainty of flow-measurement less than 0,1%. The newly developed interfaces VMK2000+ were selected as interface between specimens and the network based process control. Integrated databases for static and streaming real time data serve not only the test process- and result-data, but as well the complete administrative data for tests and QM.

Time-stamped model predictive control: an algorithm for control of processes with random delays

This paper introduces an enhancement to the model predictive control (MPC) algorithm to address to address variable time delays that may occur in the control loop. These variable delays can arise from various sources such as measurement delays, human-in-the-loop, and communication delays. The specific focus of this paper is to investigate the effect of random communication delays on network-based process control systems. The process industry is moving towards sensor and control systems that are accessed over networks rather than hardwired. For fast responding processes, the communication delay introduced by the networks can lead to performance degradation and even instability. Our experimental characterization of network communication delays revealed that they were mostly white, had a baseline minimum and approached wide-tailed distributions. We propose the time-stamped model predictive control (TSMPC) algorithm, an extension to MPC that uses a communication delay model, along with time-stamping and buffering to improve reliability over networked control systems. Experimental validation of this new algorithm resulted in improved performance over traditional MPC. Where time-stamping is not possible, accounting just for the mean/median communication delay can still result in better performance, and this simplification to TSMPC is termed as the mean/median delay model predictive control (MDMPC). Future enhancements that are possible include addressing non-linearity, input constraints, disturbance rejection, and model mismatch within the algorithm as well as to characterize and tune for delays from other control loop elements.

The use of multilayer neural networks in material synthesis

This paper demonstrates the incorporation of a multilayer neural network in semiconductor thin film deposition processes. As a first step toward neural network-based process control, we present results from neural network pattern classification and beam analysis of reflection high energy electron diffraction RHEED images of GaAs/AlGaAs crystal surfaces during molecular beam epitaxy growth. For beam analysis, we used the neural network to detect and measure the intensity of the RHEED beam spots during the growth process and, through Fourier transformation, determined the thin film deposition rate. The neural network RHEED pattern classification and intensity analysis capability allows, powerful in situ real time monitoring of epitaxial thin film deposition processes. Our results show that a three layer network with sixteen hidden neurons and three output neurons had the highest correct classification rate with a success rate of 100% during testing and training on 13 examples.