Similar to fuzzy logic, intuitionistic logic, and three-valued logic, in which a variable \(x\) is described by the triple values \(\mathbf{x}=(t,\,i,f)\), where \(t\) stands for the degree of truth, \(f\) for the degree of false, and \(i\) stands for the degree of indeterminacy, neutrosophic logic is an extension of these approaches. A neutrosophic set has been considered the next evolution of the fuzzy set for indeterminacy membership values that are absent from the vague set. Compared to the vague and fuzzy sets, the neutrosophic set's three memberships are more expressive for making decisions. Only partially complete or inconsistent information is outside the scope of fuzzy set theory's ability to process. The purpose of the mathematically developed model known as neutrosophic logic (NL) is to account for several kinds of uncertainty, including ambiguity, inconsistency, redundancy, and incompleteness [14, 15, 16]. The neutrosophic controller is a novel system that, in comparison to its fuzzy analogs, is both more broadly applicable and more tolerant of indeterminacy. Like their fuzzy analogs, neutrosophic systems might benefit from the expertise of human operators. Tofurther complicate matters, it is very unlikely that the data received by the system would be wholly complete and determinate, making it impossible to create a precise mathematical model that would represent system behavior. Non-linearity, time-varying processes to be regulated, significant unexpected external disturbances, deteriorating sensors, and other challenges to collecting accurate and trustworthy measurements all contribute to data that is incomplete and uncertain [17; 18; 19]. The concept of the range of neutralities is absent from fuzzy logic controller and allied logics because the focus of these logics is only on the membership or non-membership of a particular element to a certain class, and thus cannot account for the uncertainty that may arise in the data collection process for the reasons given above. Therefore, a neutrosophic controller is offered to cope with such circumstances when there is a chance of indeterminacy and incompleteness in the collected data [20; 21]. The basic inference process of fuzzy logic and neutrosophic logic is shown in Figure 1.  
Neutrosophic controllers are suggested to consist of four modules. (a) neutrosophic module, (b) neutrosophic rule base, (c) neutrosophic engine, and (d) de-neutrosophication module. First, data is gathered on all the factors that matter for the process under management. Second, we use the truth, falsity, and indeterminacy membership functions of neutrosophic sets to capture the truth, falsity, and indeterminacy of the obtained measures. The process at this point is known as neutrosophication. Third, the inference engine uses the neutrosophied measurements to assess the control rules recorded in the neutrosophic rule base. The results of this analysis will define one or more neutrosophic sets over the space of all potential actions. The fourth and last module of the cycle involves collapsing this neutrosophic set into a single (crisp) value using a triplet format like \(x\) (\(t,i,f\)), which would be the most accurate representation of the derived neutrosophic set. De-neutrosophication is the term for this procedure [22; 23; 24; 25; 26].