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. Most observe the process' reaction to a disturbance and set their tuning parameters accordingly. However, no two go about accomplishing those tasks in the same way.

'Heuristic' self-tuners, for example, attempt to duplicate the decision-making process of an experienced operator. They adjust their tuning parameters according to a series of expert tuning rules such as 'IF the controller overreacts to an abrupt disturbance THEN lower the derivative parameter.'

Model-based approach
A more common approach to automatic parameter selection, however, involves a mathematical 'model' of the process--an equation that relates the present value of the process output to a history of previous outputs and previous inputs applied by the controller. If the model is accurate, the controller can predict the future effect of its present efforts and tune itself accordingly.

For example, a process that reacts sluggishly to a step input can be modeled with an equation that gives the current output as a weighted sum of the most recent output and the most recent input. A self-tuner can choose the weights in that sum to fit the model to the observed process behavior. With the model in hand, the self-tuner can go on to determine how much proportional, integral, and derivative action the process can tolerate. In the case of a sluggish process, the model will show that the controller is free to apply aggressive control efforts. The self-tuner will then set the P, I, and D parameters to relatively high values.

Variations on the theme
Exactly how high or low the tuning parameters should be set depends on the performance objectives specified by the operator. If, for example, the settling time is to be limited to some maximum value, the required tuning parameters can be determined by analyzing the time constant and the deadtime of the process model. On the other hand, if excessive overshoot is the operator's principal concern, the controller can be configured to select tuning parameters that will limit the rate of change of the process variable.

Self-tuning controllers also differ in their data collection techniques. Some apply a series of artificial disturbances to the process in order to observe how it behaves. Others make do with data collected during normal loop operations. The latter approach limits the waste and inconvenience caused by intentionally disturbing the process, but generally produces much less useful information about the process' behavior.

Which of these many variations is appropriate for a given application of self-tuning control is up to the operator. A single universally applicable technique has yet to be developed.

No comments
Consulting-Specifying Engineer's Product of the Year (POY) contest is the premier award for new products in the HVAC, fire, electrical, and...
Consulting-Specifying Engineer magazine is dedicated to encouraging and recognizing the most talented young individuals...
The MEP Giants program lists the top mechanical, electrical, plumbing, and fire protection engineering firms in the United States.
Salary survey: How much are you worth?; Dedicated outdoor air systems; Energy models and lighting
Fire, life safety in schools; Fire protection codes; Detection, suppression, and notification; 2015 Commissioning Giants; Emergency and standby power in hospitals
HVAC and building envelope: Efficient, effective systems; Designing fire sprinkler systems; Wireless controls in buildings; 2015 Product of the Year winners
Designing positive-energy buildings; Ensuring power quality; Complying with NFPA 110; Minimizing arc flash hazards
Implementing microgrids: Controlling campus power generation; Understanding cogeneration systems; Evaluating UPS system efficiency; Driving data center PUE, efficiency
Optimizing genset sizing; How the Internet of Things affects the data center; Increasing transformer efficiency; Standby vs. emergency power in mission critical facilities
As brand protection manager for Eaton’s Electrical Sector, Tom Grace oversees counterfeit awareness...
Amara Rozgus is chief editor and content manager of Consulting-Specifier Engineer magazine.
IEEE power industry experts bring their combined experience in the electrical power industry...
Michael Heinsdorf, P.E., LEED AP, CDT is an Engineering Specification Writer at ARCOM MasterSpec.