## 6 Implementation of the Dynamic Fuzzy Logic Controller (Dynamic FLC)  
In addition to this static fuzzy model, the dynamic behavior for this metaheuristic FLC is given through the dynamic PSO mechanism, whereby the PSO parameters are optimized dynamically via the predefined TSK-FLC. Figure 2 illustrates that all three main controllers are combined and synchronized together to act on the same sample instant \"\(k\)\". Due to the non-linear parameter variations (uncertainties) of the electric rover, the proposed metaheuristic FLC should have the ability to quickly optimize the fuzzification process, de-fuzzification process and fuzzy reasoning process. Dynamic Fuzzification Process for the Fuzzy Antecedent and Consequent Dimensions with the Dynamic PSO Mechanism  
Figure 6 shows that when designing and developing the fuzzy antecedent and consequent dimensions for the proposed metaheuristic FLC, interval-valued fuzzy sets were taken into consideration. In these types of fuzzy sets, for any given input \(x\), the membership in this fuzzy set \(\widetilde{A}\) can be expressed as \(\mu_{A}(x)\) for the membership interval from \(\lambda_{1}\) to \(\lambda_{2}\). To obtain more robust behavior for these types of fuzzy sets, the fuzzy intervals become fuzzy. These types of fuzzy functions can be represented by the \"\(n\)\" number of ordinary fuzzy sets. Therefore, these are called interval-valued fuzzy sets or type-2 fuzzy sets. These fuzzy sets can be expressed as (20):  
\[\widetilde{A}:X\rightarrow\varepsilon([0,1]) \tag{20}\]  
In this fuzzy set \(\widetilde{A}\), \(\varepsilon([0,1])\) includes all of the closed intervals of real numbers in [0,1]. Therefore, this can be represented as (21):  
\[\varepsilon([0,1])\subset\Re \tag{21}\]  
In this case, Figure 6 shows that for each value of \(x\), in \(\widetilde{A}(x)\) the membership grade is given by the shaded area enclosed by the membership curves \(f_{1}\) and \(f_{2}\) When \(x=a\), \(f_{1}\rightarrow\lambda_{1}\) and \(f_{2}\rightarrow\lambda_{2}\). Therefore, \(\lambda_{1}\) and \(\lambda_{2}\) are the upper bound limit and the lower bound limit for \(\widetilde{A}(a)\) when \(x=a\). In fuzzy-logic-based systems, the accuracy is critically dependent on the capabilities of designing and constructing the appropriate membership functions. However, reasonable values between the upper bound and the lower bound limits are selected. That means that the uncertainty behavior is not taken into consideration. In practice, this causes more speed fluctuations, due to the rapid changes that take place in the desired input trajectory. As per the observed data, interval-valued fuzzy linguistic variables are more capable of capturing the uncertainty behavior of \(E_{k}\) and \(dE_{k}\). This is a more accurate process compared to using crisp variables. However, the disadvantage of these interval-valued membership functions compared to the ordinary fuzzy sets is that they are computationally more demanding and sophisticated. Practically, programming libraries and toolboxes are not available. This is because most modern day-to-day fuzzy-logic-based applications are not very sensitive to minor changes in the predefined fuzzy membership functions. However, the developed electric rover should respond to minor changes in the controlled parameters affecting the power optimization and stability of the rover. Figure 6 shows that this interval-valued fuzzy set could be represented by two ordinary symmetric static Gaussian curve membership functions.