Modeling and Control

In the early 1990’s, I helped establish Emerson’s advanced control program. As part of this initiative, a technical advisory committee was formed to periodically review the program and to help provide technical direction. This committee was made up of leaders in process control such as Professors Karl Astrom, Lund University, Tom Edgar, University of Texas, and Tom McAvoy, University of Maryland. Some of the initial products that came out of this research and development program were initially introduced in the Provox and RS3 product lines. Later, with the introduction of DeltaV, these products for control tuning, fuzzy logic control, model predictive control, and property estimation were embedded in the DeltaV system.

The DeltaV architecture supports abstraction of system software components from the physical hardware. This capability allowed the advanced control team to introduce a family of control system simulation products, DeltaV Simulate. These products allow all system features to be combined and executed on a single workstation or distributed between multiple workstations without the requirement for a physical controller.

While Greg McMillan was at Solutia, he and I often interacted on beta tests of advanced control products and the control system simulation environment supported by DeltaV.

The technical basis for the advanced control and simulation products available in DeltaV are described in a series of over thirty technical papers. These papers were written and presented by the advanced control team at various control conferences. As customers applied these products we received detailed technical questions that were often best addressed by referring the customer to one or more of these technical papers. Thus, Dr. Willy Wojsznis, Emerson Process Management, and I began to discuss writing a book that incorporated information from these papers that could serve as a reference for control engineers working in this area. In addition, we felt it was important to address the benefits and application of this technology on various processes. Thus, we invited Greg McMillan and Mike Brown to co-author this book. The book, Advanced Control Unleashed, was published in the fall of 2003 and that year was ISA’s best selling book

If you are interested in exploring the benefits of advanced control, then Advanced Control Unleashed is a good starting point in learning about this technology. With ISA’s permission, I have created a brief overview of the book.

Overview of Advanced Control Unleashed

You can purchase this book directly from ISA. As stated in the book introduction, all royalties from the books are donated by the authors to “universities, consortia, and educational programs to promote and enhance the development and use of advanced process control.”

Terry described an innovative technique of using the PID block for combining split ranged control and valve position control (see Terry’s Oct 16 entry). This technique eliminates the limit cycle at the split range point caused by the increase in nonlinearities and the decrease in resolution imposed by backlash, backfilled pipes and dip tubes, rangeability limits, and friction particularly associated with starting a flow from zero. This technique also eliminates the conceptual and tuning problems with valve position control. People tend to confuse valve position control with valve positioners or digital valve controllers. The tuning of the integral-only controller for valve position control is much more critical than most people realize to prevent interaction but provide a fast enough response to reject large load upsets. The best quantitative analysis I have seen on the severity of the tuning issues with “valve position control” is the article by Cheng-Ching Yu and William L. Luyben titled “Analysis of Valve-Position Control for Dual-Input Processes” (Ind. Eng. Chem. Fundam. Vol. 25, No. 3, 1986 pp 344-349).

Instead of a special network for PID control, a standard Model Predictive Control (MPC) block can be configured to eliminate the need for split ranged control and valve position control. The MPC is simply set up for two manipulated variables (MV), one controlled variable, and one optimization variable. The optimization variable is the manipulated variable that provides the finest control (e.g. set point of the fastest and most precise control valve or variable speed drive). The optimization objective is to gradually return the “fine” MV to a mid range (e.g. 50%) after helping the “coarse” MV reject a load upset or minimize overshoot of a new set point. To insure the optimization takes a back set to tight regulation and set point response of the controlled variable, the “penalty on error” (PE) of the optimization variable is decreased (e.g. optimization variable PE=0.1).

When the MV have different process dynamics, the advantage of MPC is greater. By the automatic identification and incorporation of the MV dynamics in MPC, better feedback, feedforward, and constraint control is possible. The longer term view of the MPC also makes it less sensitive to resolution limits. Additionally, the “maximum MV rate” parameter can be written to zero when the controlled variable is close enough to set point to eliminate the limit cycle from the “coarse” MV. The following white paper discusses in more detail this use of a MPC to eliminate split ranged control and valve position control. The article titled “A Fine Time to Break Away from Old Valve Problems”, in the November 2005 issue of Control magazine provides more background and a perspective.

White Paper on Dual MV MPC

My first exposure to model predictive control, MPC, was in late 1979 when I attended a meeting called by Bob Otto, ISA Fellow. Bob had just returned from the AIChE 86th Annual National Meeting where he sat in on Charlie Cutler and Ramaker’s presentation of their paper Dynamic matrix control-a computer control algorithm. This landmark work by Shell was the fore runner of modern day model predictive control, MPC. Bob’s assessment was that this technology represented one of the most important developments he had seen in process control. The power of MPC technology comes from the fact that the controller is generated based on a process step response or impulse response model and is designed to minimize the control error over a prediction horizon. Control performance is determined by parameters that specify penalty on error and penalty on move. Soon after Shell’s public announcement of their work on dynamic matrix control, Charlie went on to form the DMC Corporation. Since that time, major suppliers of MPC technology have successful addressed a variety of applications. The wide spread acceptance of MPC technology is well documented in the paper by Professors Joe Qin and Tom Badgwell, A survey of industrial model predictive control technology.

In the early-80’s, Bob Otto lead an initiative within Emerson to explore the feasibility of embedding MPC technology within a distributed control system. This research focused primarily on single loop applications as documented in the paper Development of a Multivariable forward modeling controller by Bob Otto and Kelvin Erickson. Field trails were conducted using a prototype of single loop MPC. One of the technical challenges that prevented general deployment of this technology at that time was the need to provide a robust means of process identification. Also, it was not feasible at that time to embed general MPC in the controller because of the associated CPU and memory requirements.

By the later-90’s, the availability of low cost memory and vastly improved processor performance made it feasible to fully embed MPC technology within the control system. By embedding MPC in the control system, a control system supplier can provide an environment that makes it easier and quicker to engineer and commission MPC applications. Also, by embedding MPC in the controller, it is possible to address applications that require faster control execution e.g. 1sec period of execution. In many cases, embedded MPC control is a valid alternative to the traditional PID based strategies for deadtime compensation, feedforward and override control. If you have no experience with MPC, then some examples of how MPC may be effectively used to replace traditional PID based strategies are contained in the following:

MPC for smaller applications

These examples are based on the DeltaV MPC capability introduced in 2000, DeltaV Predict. This initial capability was targeted at smaller applications (no larger in size than 8x8). The DeltaV advanced control team later developed DeltaV PredictPro to address larger applications (as large as 40x80 in size).

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 we later patented. In the summer of 2000, we sponsored a graduate student under the guidance of 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.

Adaptive Technology

Also, additional detail can be found in the two patents that we have on the basic technology and its use with non-linear applications.