Prof. Sigurd Skogestad

Department of Chemical Engineering
Norwegian Univ. of Science and Technology (NTNU)


Optimality of PID control for process control applications


Although the proportional-integral-derivative (PID) controller has only three parameters, it is not easy, without a systematic procedure, to find good values (settings) for them. In fact, a visit to a process plant will usually show that a large number of the PID controllers are poorly tuned. In general, excellent results are obtained if one is willing to invest some time and take a systematic approach and invest some time. The following two-step procedure works well, at least for typical stable processes encountered in the process industry: Step 1. Obtain a first- or second-order plus delay model. Step 2. Derive model-based SIMC controller settings. With the SIMC method, PI-settings result if we start from a first-order model, whereas PID-settings result from a second-order model. The SIMC method is based on classical ideas presented earlier by Ziegler and Nichols (1942), the IMC PID-tuning paper by Rivera et al. (1986), and the closely related direct synthesis tuning rules in the book by Smith and Corripio (1985). The Ziegler-Nichols settings result in a very good disturbance response for integrating processes, but are otherwise known to result in rather aggressive settings (Tyreus and Luyben 1992) (Astrom and Hagglund 1995), and also give poor performance for processes with a dominant delay. On the other hand, the analytically derived IMC-settings of Rivera et al. (1986) are known to result in poor disturbance response for integrating processes (Chien and Fruehauf 1990), (Horn et al. 1996), but are robust and generally give very good responses for setpoint changes. The SIMC tuning rule works well for both integrating and pure time delay processes, and for both setpoints and load disturbances. It is actually close to the optimum as can be seen by evaluating the Pareto-optimality of the SIMC method with respect to he conflicting objectives of performance and robustness. The results with PID control are generally also better than with the model-based Smith Predictor, even with processes with large time delays. This is surprising, and it shows that if one puts enough effort into the PID tuning then there is little benefit in considering more complex controllers, including MPC.

Brief Biography:

Sigurd Skogestad is a professor in chemical engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim. Born in Norway in 1955, he received the Siv.Ing. degree (M.S.) in chemical engineering at NTNU in in 1978. After finishing his military service at the Norwegian Defence Research Institute, he worked from 1980 to 1983 with Norsk Hydro in the areas of process design and simulation at their Research Center in Porsgrunn, Norway. Moving to the US and working 3.5 years under the guidance of Manfred Morari, he received the Ph.D. degree from the California Institute of Technology in 1987. He has been a full professor at NTNU since 1987. During the period 1999 to 2009 he was Head of Department of Chemical Engineering ( Kjemisk prosessteknologi ). He was at sabattical leave at the University of California at Berkeley in 1994-95, and at the University of California at Santa Barbara in 2001-02. The author of about 200 international journal publications and 200 conference publications, he is the principal author together with Ian Postlethwaite of the book "Multivariable feedback control" published by Wiley in 1996 (first edition) and 2005 (second edition). Dr. Skogestad was awarded "Innstilling to the King" for his Siv.Ing. degree in 1979, a Fullbright fellowship in 1983, received the Ted Peterson Award from AIChE in 1989, the George S. Axelby Outstanding Paper Award from IEEE in 1990, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council in 1992, and the Best Paper of the Year 2004 Award from Computers and Chemical Engineering. He was an Editor of Automatica during the period 1996-2002 and is member of the IFAC Technical Board for the period 2008 to 2014. He is a Fellow of the American Institute of Chemical Engineers (2012) and was elected into the Process Control Hall of Fame in 2011. Professor Skogestad has graduated 34 PhD candidates (1990-2012). He presently has a group of about 6 Ph.D. students and is the Head of PROST which is the strong point center in process systems engineering in Trondheim and involves about 50 people in various departments. The goal of his research is to develop simple yet rigorous methods to solve problems of engineering significance. Research interests include the use of feedback as a tool to (1) reduce uncertainty (including robust control), (2) change the system dynamics (including stabilization), and (3) generally make the system more well-behaved (including self-optimizing control). Other interests include limitations on performance in linear systems, control structure design and plantwide control, interactions between process design and control, and distillation column design, control and dynamics. His other main interests are mountain skiing (cross country), orienteering (running around with a map) and grouse hunting.