NonLinear Gain Variations

Process Characterization (Model) for Control

AI-PID Optimal Tuning can best be understood based on the following metahporical equation

E=P*C**2

At PIDAnalytix, we have devised the method of AI-PID tuning that reliably and robustly tune and re-tune PID no matter how little is "known" about P and when changes happen. One of the most affecting process parameter is process gain G. Its variation affect E considerably. Basically, G defines change in P when OP is manipulated in open loop. However, when G changes later causing the loop to oscillate it becomes extremely difficult to know its value correctly. It is not always possible or permissible to re-identify value of G in closed loop operation. Consequently, majority of PID loops oscillate to a varying degree and remain so until it becomes unbearable.

AI-PID tuning as demonstrated later show how re-tuning can be done in real time in closed loop. PIDAnalytix has devised adaptive intelligent (AI) method of re-tuning PID loops as and when needed. The art of perfecting PID tuning is what makes AI-PID so unqiue and powerful.

To demonstrate, capability and agility of AI-PID, we have included two sets of demonstration, one relating Random NonLinear Gain variation and another relating to Random Valve Stiction (deadband in particular).

There is no known demonstration in last 100 years of PID tuning of such wide variations and perfect control.

Welcome to witness a new dawn of AI-PID tuning to marvel at what can be done with mighty PID. Incidentally, simplicity of PID as a controller is what underlines adaptability of AI-PID. AI-PID demonstrates enduring power of PID arising from its simplicity.