Vance J. VanDoren, consulting editor


PID, APC August 1, 2003

Model-predictive control looks to the future

A model-predictive controller uses a mathematical model of the process to predict future effects of current control actions. Knowing where the process is headed and how the process variable reacts to the controller's actions gives the controller the foresight to push the process in the right direction.

By Vance J. VanDoren, consulting editor
PID, APC March 1, 1999

Distributed control with programmable controllers

Distributed control systems (DCSs) have long been a staple of the industrial automation industry. They are powerful, extensible, and capable of running real-time control programs uninterrupted for years on end. Unfortunately, DCSs also tend to be unique to each vendor, built from proprietary hardware available from only one source.

By Vance J. VanDoren, consulting editor
Control Systems August 1, 1998

Ziegler-Nichols tuning methods

Tuning a proportional-integral-derivative (PID) controller is a matter of selecting the right mix of P, I, and D action to achieve a desired closed performance (see ' Basics of Proportional-Integral-Derivative Control ,' Control Engineering, March 1998). The ISA standard form of the PID algorithm is: The variable CO(t) represents the controller output applied to the process at time t, PV(t) is the process variable coming from the process, and e(t) is the error between the setpoint and the process variable. Proportional action is weighted by a factor of P, the integral action is weighted by P/T I , and the derivative action is weighted by PT D where P is the controller gain, T I is the integral time, and T D is the derivative time. In 1942, John G. Ziegler and Nathaniel B.

By Vance J. VanDoren, consulting editor
Control Systems September 1, 1997

Self-Tuning Controllers Auto-Select P, I, D Values

Tuning a PID controller is conceptually simple--observe the behavior of the controlled process and fine tune the controller's proportional (P), integral (I), and derivative (D) parameters until the closed-loop system performs as desired. However, PID tuning is often more of an art than a science. The best choice of tuning parameters depends upon a variety of factors including the dynamic behavior of the controlled process, the controller's objectives, and the operator's understanding of the tuning procedures. Self-tuning PID controllers simplify matters by executing the necessary tuning procedures automatically.

By Vance J. VanDoren, consulting editor
IIoT, Industrie 4.0 June 1, 1997

Overcoming the deadtime dilemma

Arguably the trickiest problem to overcome with a feedback controller is process deadtime--the delay between the application of a control effort and its first effect on the process variable. During that interval, the process does not respond to the controller's activity at all, and any attempt to manipulate the process variable before the deadtime has elapsed inevitably fails. Deadtime occurs in many different control applications, generally as a result of material being transported from the site of the actuator to another location where the sensor measures the process variable. Not until the material has reached the sensor can any changes caused by the actuator be detected.

By Vance J. VanDoren, consulting editor
PLCs, PACs May 1, 1997

Bode plots solve frequency domain problems

Every child who has ever held a spring upright knows that tugging on the top end causes the bottom end to start bouncing and that repeated tugging keeps those oscillations going. Some may notice that even though both ends always oscillate at the same frequency, the bottom end bounces higher at some frequencies than at others. Truly gifted children might even notice that the bottom end oscillates out of sync with the top end and lagsfurther and further behind as the frequency increases. Engineers know that many mechanical, electrical, and chemical processes with energy-storing components behave the same way.

By Vance J. VanDoren, consulting editor
Diagnostics, Asset Management February 1, 1997

Velocity Is Key to Motion Control

Motion control systems are generally designed to move a load along a specified path as fast as possible without damaging the load or the mechanism driving it. Heavy loads are particularly difficult to control since inertia tends to force the load off course during high acceleration maneuvers. Worse still, the stress of resisting the load's inertia can destroy not only the drive mechanism, but the load itself. Rotating machinery, linear conveyors, and multiaxis robotic arms all share these problems, but a more familiar example of heavy-load motion control is the passenger elevator.

By Vance J. VanDoren, consulting editor
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