Is it necessary to have a thorough understanding of automatic control theory to study *automation?

Personally, I think theoretical things are still very important. With many things, one first understands the theory and then applies it under the guidance of that theory, which makes learning much easier. The transfer function in the principles of automatic control can help explain why there is a problem of residual error in proportional control. Theoretical concepts are very useful for analyzing problems.

Connecting theory with practice is the criterion for testing truth; practice requires reflection through work. Theory can be learned quickly in school, but once one leaves school, there isn’t enough energy for that

It’s impossible not to remember what one has learned, but forgetting what one has learned is completely different from never having learned it in the first place. Automatic control theory is important for instrumentation technicians, whether you are engaged in installation and maintenance or design work. It’s okay to forget what you’ve learned; all that matters is that you know where to find these theories when you need them to analyze problems. Take the book and have a look – you’ll surely be able to understand it quickly, and it will help you solve problems, right?

Right now, studying the principles of automatic control feels like learning them for the first time! It seems pretty difficult.

That’s right; only by mastering the theory can you apply it skillfully in practice. Whether you are doing design or maintenance, mastering it properly is essential to achieve the goal. :handshake

As a maintenance technician, it is sufficient to have a basic understanding of control theory; if one wants to become an expert, it is better to master more advanced theoretical knowledge such as the principles of control. Since it’s this major, the poster surely has no problem with the basic theoretical knowledge. This post was last edited by zhaohh3211 on 2009-4-10 08:05.]

A lot has been said about the principles of automatic control; at its core, it is mathematics! ! If you’re really good at math, then studying the principles of control systems is like drinking cold water for you: lol. The principles of automatic control belong to classical control theory, which deals mainly with single-input single-output problems. Modern control theory, on the other hand, deals with multi-input multi-output problems. And what is used to solve these multi-input multi-output problems? It’s matrix theory from mathematics. If one has a strong grasp of matrix theory, then modern control theory becomes quite straightforward: lol. Another very important course in industrial automation is process control. In my opinion, the theory related to process control isn’t that sophisticated; it’s more practical in nature, aiming to teach people how to solve real-world problems. As for whether those solutions are correct, that is determined by applying principles of automatic control and modern control theory. :)

I don’t think there’s much need for that. Control principles are generally based on mathematical models, but how many processes can we actually model? Even professors and doctors find it difficult to do so. Therefore, in practice qualitative analysis is usually employed – for example, figuring out how to adjust when there is a large deviation or what to do in case of system oscillations. Adjustments are made step by step based on experience, and often experience proves to be more practical than theory

It is indeed difficult to develop models for industrial processes, but they are used successfully in the aerospace industry. The reason for this is that the models there are relatively clear and easier to create. Predictive control in advanced control methods was first developed based on successful applications in aerospace before being applied to industrial processes. It’s not that mechanism models cannot be created; rather, the mechanism models that are developed are too complex, and using advanced control algorithms consumes the resources of industrial computers. On the other hand, many parameters of these mechanism models cannot be measured in actual industrial settings, so it becomes necessary to simplify the models. This simplification leads to two problems: first, the models become inaccurate; second, as production proceeds, changes occur both in terms of raw materials and equipment, which raises the need to update the models regularly. However, updating models on a continuous basis is a rather risky approach. There is too much interference at the third industrial site, and the nature of that interference is unknown. All of these have had a significant impact on industrial modeling. Qualitative analysis is essentially a form of empirical analysis; experienced instrument technicians in the past simply knew how to make the adjustments needed, but they did not understand why those adjustments were necessary. Although this approach was sufficient in industrial settings, such empirical methods could not be generalized or promoted, which hindered the development of control theory.