The three historic challenges in tuning such as: single-loop, limited success of auto-tuning, modern difficulties of model-based control – all share similar root cause. The control engineering website published a two part series on the problems of auto tuning and its nature & definitions: Part 1 in the June 2018 issue and the advantages and disadvantages of autotuning control. Part 2 in August 2018 issue. Both of these articles are very informative read and these articles can make the accurate conclusions. However they miss one of the most important main implicaitons. There is one story about auto-tuning that reveals a very valuable lesson.
The articles somehow conclude that auto-tuning still has no panacea. Engineers rightfully suspect this. Engineers regard this as the most important problem or challenge that comes with this are the unpredictable and nonlinear process. The former is the one wherein the actual process response tends to differentiate from the pre-identified response – this is where the tuning or model I snow based. In return, it shows that this tends to be accurate for most of the processes. This is the very reason why autotuning only got limited success regardless of the number of industry attempts. In which the actual process response differentiates poses for a fundamental conundrum for tuning and modeling.
In addition to this, it helps to understand to explain the reason why single-loop tuning and multivariable control modeling most likely to recur maintenance in practical applications. In theory, they should be one-time engineering tasks. In this case, this is the long-held reality of loop tuning and now, it has emerged as the reality of the model-based control too.
There are two popular solutions to these problems. But these solutions do not guarantee absolute answers to these problems. One solution idea deals with average model or average tuning. Most engineers regard this as the best way to deal with this problem, however it did not solve the problem engineers have today. The second idea for solution is the autotuning or adaptive modeling. This process has a potential to bring more problematic solutions than averaging, because the basic tuning for today may not be applicable or appropriate tomorrow.
In the vernacular sense, the process gains are subject to change. Majority – if not all – gains can be frequently or dynamically changed because of the presence of everyday disturbances in some process conditions. The returning and remodeling remain as the commonplace as they do. Adding to the limited success of autotuning, engineers testify to this. It is a common sense that people spent years doing troubleshooting process to control its performance. However, autotuning cannot solve this recurring problem. The users should take a look at the existence of the emergence of adaptive modelling, which in case attempts to do the very same thing on a larger scale – now with a critical eye.