When we first started Emerson’s advanced control program in the early 90’s, one of the initial objectives of the program was to develop an adaptive control capability that could be used in our control products. However, we realize that adaptive control is one of the most challenging advanced control areas to address from a technical standpoint. Thus, most of the programs resources were initially focused on other areas e.g. on-demand tuning, property estimation using neural networks, simulation, fuzzy logic control and model predictive control. Adaptive control was kept on the backburner for many years with work in this area restricted to technical evaluation of different technologies. Gradually, starting in the late 90’s, a more focused effort was put into addressing adaptive control. As a result of this work, the first release of our adaptive control technology was recently introduced as part of the DeltaV Insight product in the v9.3 release. The things that we learned in researching and developing this technology greatly influence the final design of DeltaV Insight.
In the early 90’s, one of the first adaptive control technique that we investigated was one developed by Professor Karl Astrom, Lund University. This technique allows the controller gain to be automatically adapted through on-line assessment of process gain. As part of this investigation, we worked with Professor W. K. Ho from the National University of Singapore in researching this technique. Even though the approach proposed by Astrom is technically very sound and is utilized in some commercial products, its application is limited to feedback control and adaptation of controller gain. Since our ultimate goal was to find a technique that could be used to adapt all components of PID feedback control (Gain, Reset, and Rate) and feedforward control (gain, Lead/Lag Time constant, and deadtime), we did not pursue this approach past this initial investigation.
At one point we were offered the rights to an adaptive control technique that had been developed by the engineering department of a major chemical company. To avoid polluting the Emerson development team, we hired an outside consultant to evaluate this technology. It turns out that the technique was based on pattern recognition and the application of rules to establish tuning. Even though this approach is used by some major process control companies, the feedback from customers who had tried this technology was not encouraging. There were reports of erroneous adjusted of controller tuning base on cyclic upstream disturbances that were interpreted as a sign of too much controller gain. Thus, we decided to avoid this approach.
In the late 1990’s, Willy Wojsznis came across a very interesting paper on model free adaptive control. This paper helped sparked work that lead to a unique design and implementation of model free adaptive control that Professor Dale Seborg, University of California at Santa Barbara, UCSC, to test and further investigate this technique using process simulations. The basic approach provided to be a reliable method for directly establishing feedback tuning. However, only through inference from the controller tuning was it possible to gain any insight into the process gain and dynamics. Also, the method could only be used for the adaptation of feedback tuning. Therefore, we continue to evaluate other approaches that better met our requirements and would give direct insight into the process gain and dynamics.
In the mid-90’s, a number of papers on the application of controller switching appeared in some of the major control conferences as a technique for evaluating best tuning. Also, a few papers were published on the use of model switching to identify process gain and dynamics. The concept as proposed was not practical to implement. However, these techniques offered the promise of allowing process models to be identified for both the feedback and feedforward path. After some consideration, Willy and I developed a new approach which we labeled model switching with interpolation and re-centering. This new approach to model switching required the evaluation of only a limited number of models at any given time. Testing of this technique by UCSB from 2001-2003 showed the method to converge very quickly for a variety of self-regulating and integrating processes.
An alpha version of adaptive control based on model switching with interpolation and re-centering was installed at two chemical plants in early 2004. The results from one of these sites, Solutia, were published in September 2004 issue of Chemical Processing. Based on the positive results of these installations, beta testing was conducted at four sites from 2005-2006 on approximately 1000 loops. As part of this beta testing, a special emphasis was place on quantifying the benefits of adaptive control for the batch industry. We created a video of the Lubrizol installation in which the customer discusses the benefits they realized from adaptive control on their batch process. The things we learned from these beta installations had a great impact on the final product design. In particular, the beta test proved the value of maintaining a record of the models that are identified over time from each loop. Also, the capability to automatically provide tuning recommendation using this technology was seen as a major benefit in improving plant operations independent of whether closed loop adaptive control was applied in the plant.
If you have an interest in learning more about the adaptive modeling technique used in Delta Insight, then the following presentation that Willy Wojsznis and I gave at Emerson Exchange provides information on the technical details on this technology.
Also, additional detail can be found in the two patents that we have on the
etacgi ph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&co1=AND&d=PTXT&s1=blevins.INNM.&s2=(tx.INST.)&OS=IN/blevins+AND+IS/tx&RS=IN/blevins+AND+IS/tx”>non-linear applications