What is Lambda Tuning?

Adjusting a Single Tuning Parameter for Optimal Control Loop Performance

Tuning a PID controller should always start with a clear understanding of both the control objective and the Design Level of Operation (DLO).  Unfortunately a subsequent step (the calculation of tuning parameters) can often put practitioners in a bind relative to achieving the designated objective. The values for Proportional, Integral and Derivative tuning parameters may or may not produce the type of controller responsiveness that’s needed for success. Depending on the tuning correlation or method used they often result in a response that is either too sluggish or too aggressive.

Internal Model Control (IMC) is an offshoot of the widely used Lambda tuning correlations and it directly addresses the challenge of poor controller responsiveness specifically in cases involving a large Dead-Time. Once a control loop’s dynamics are modeled the IMC correlations permit practitioners to make fine adjustments to a single value, the Closed Loop Time Constant. Those adjustments make possible an array of response options ranging from conservative to aggressive. A profile of each response can be easily simulated, showcasing important attributes such as overshoot and setting time. Equipped with these insights practitioners are able to specify a response profile prior to implementing new tuning parameters.

• Change Is A Comin’

A process’ dynamics change much like a control loop’s purpose. Consider a heat exchanger that corrodes and fouls over time impacting the efficiency with which heat is transferred.  How about the purpose of a temperature loop (or any type of loop) whose control objective changes as the raw material, the end-product, or another attribute evolves. Those changes can be subtle. They can be significant. IMC tuning allows practitioners to explore the full range of responses and to specify parameters that best suit their purpose.

• Not for Nothing

Concerns with IMC have historically centered on its tendency toward conservative and over-damped responses.   Sluggishness is often a poor choice when applied to PID control loops that are tuned for disturbance rejection. Such loops are ill prepared to handle the delayed effects of load changes and can become unstable. Even so, IMC has evolved and the industry trend over the past many years has been to adopt it and other correlations based on IMC.

• Same Problem, New Approach

Praise of IMC widely focuses on its value in reducing process variability. Recent innovations in the modeling of highly dynamic process data, however, expand IMC’s application value beyond fast responding control loops. Time-based PID tuning software capable of modeling non-steady state conditions solves the issues associated with large Dead-Time and oscillatory tendencies that originally hampered IMC.

Like other offshoots of Lambda, IMC generates controller tuning parameters that are highly stable and easily customized through adjustment to the Closed-Loop Time Constant.  Combined with other related innovations in PID controller tuning IMC equips practitioners with a highly versatile approach to process control and plant optimization.