How does fuzzy logic control differ from traditional feedback control in handling non-linear process dynamics?

Fuzzy logic control differs from traditional feedback control in handling non-linear process dynamics by using linguistic variables and fuzzy rules to mimic human reasoning, whereas traditional feedback control relies on mathematical models that are often linearized approximations of the process. Traditional feedback control, such as PID (Proportional-Integral-Derivative) control, works well for processes that are approximately linear or can be reasonably linearized around an operating point. However, many processes in a hydrogen production plant exhibit significant non-linear behavior, meaning that their response to changes in input varies depending on the operating conditions. Linearizing these processes can lead to inaccurate control and poor performance. Fuzzy logic control, on the other hand, does not require a precise mathematical model of the process. Instead, it uses fuzzy sets to define linguistic variables, such as "temperature is high" or "pressure is low." Fuzzy rules, expressed in the form of "IF (condition) THEN (action)," relate these linguistic variables to control actions. For example, a fuzzy rule might be "IF temperature is high THEN decrease fuel flow slightly." These rules are based on expert knowledge or empirical observations of the process behavior. Because fuzzy logic control does not rely on a linearized model, it can effectively handle non-linear process dynamics and provide robust control over a wide range of operating conditions. It also allows for incorporating qualitative knowledge and human expertise into the control strategy.