A fuzzy PID controller uses fuzzy logic software to control errors in industrial systems, providing more accurate results in non-linear situations. It calculates deviation over an analog range and maps errors based on persistence. Defuzzification resolves contradictory conclusions. It can provide different levels of response and function as a standard PID controller.
A fuzzy PID controller is a proportional-integral-derivative controller that uses fuzzy logic software principles as a form of error control in industrial systems. PID controllers are used extensively in automation to adjust valves and other process controls based on the accumulation of errors over time. The difference between a fuzzy PID controller and a standard PID controller is in the ability to provide more accurate results in non-linear situations. Fuzzy systems are built on a type of programming logic that attempts to address gray areas of uncertainty in processes more effectively than standard controls.
Control systems must have a built-in method for reporting an out-of-range value back into an acceptable range. Typical PID controllers will have an on/off response to this variability at predetermined points in the process, which can be compared somewhat to a digital method of looking at a process, breaking it down into discrete values and assigning predetermined actions to those values. A fuzzy PID controller, on the other hand, calculates the deviation over an analog range where there is an optimal value and increasingly suboptimal values, but no predefined point where an action is always taken.
The design of a fuzzy PID controller and a standard PID controller both use historical values to calculate future responses. The letters in PID indeed represent this, where P represents present errors, I for past errors, and D as future error states. Fuzzy systems attempt to map errors in terms of persistence and assign them to various membership sets for different ranges of logical conditions. This allows a fuzzy PID controller to also set the slew rate to bring a system back under control. This rate of change is based on rules of inference, where the accumulation of data and error states suggests a more correct course of action.
One of the problems with a fuzzy PID controller is that it can come to contradictory conclusions and take no action. This requires a process of resolving conflicts in code which is commonly referred to in fuzzy systems as defuzzification. Defuzzification is done by giving certain parameters in a PID controller more weight than others to tip the scales in a certain direction of action, and this is analogous to a tuned gain in a standard PID controller.
In the case of standard variation, a fuzzy PID controller and a standard PID controller can both adjust the control system in exactly the same way. This does not suggest that they are identical control systems or that the benefits of fuzzy control have been disproven. It simply means that the situation is easily handled by any basic control system. Standard PID controllers can be viewed as a subset of the fuzzy PID controller, which has a more robust capability and is capable of handling unpredictable deviations. In situations where standard PID controllers fail, a well-designed fuzzy PID controller will work better.
The benefits of a fuzzy PID controller include that it can provide different levels of response to non-linear variations in a system and, at the same time, it can function as a standard PID controller in a system where variation is predictable. A fuzzy PID controller can also keep a more stable system. It can be weighted by response types just like the gain settings on a standard PID controlled system.
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