## 9 Results and Discussion  
Straight, incline, downhill and curving roads were used to examine the performance of the built dynamic metaheuristic FLC with all sorts of road limits during the quickest acceleration and deceleration phases. This article, on the other hand, focuses on road testing conducted on straight roads under various traction conditions (such as on slippery wet grass surfaces), as shown in Figures 11 and 12. When the rover moves along a straight path (trajectory), even under varied traction environments (different friction coefficients \(\mu_{j}\)), the orientation of the rover should remain constant throughout time. Correspondingly, in order to maintain a certain intended orientation based on the observed data from the installed gyroscope, the built dynamic metaheuristic FLC should synchronize all of the angular velocities (r.p.m.) of each wheel in order to retain the desired orientation (fixed orientation) (because the developed rover turning method based on the differential speed technique is not discussed in this paper). Figure 13 depicts the desired throttle signal and the desired steering angle, which indicate a zero-degree yaw angle (in its neutral position) during the straight road test operating time period (fed wirelessly based on the IEEE 802.15.04 protocol through a computer-controlled joystick). Figure 14 shows that the \(\mu_{j}\) fluctuation over time, as calculated by Equation (7) in [104], is less than 0.4, while the average \(\mu_{j}\) is about 0.12. This means that the wet grass road surface is extremely slick. Consequently, in all of these situations, optimizing the wheel slip of each wheel and maintaining the desired yaw angle across the operating time period proved very complex. Figure 15 depicts the r.p.m. of each wheel in relation to the required average r.p.m _refWs_. Table 11 shows the steady-state error \(E_{ss}\) and steady-state error as a percentage \(E_{ss}\)% for the peak edge of the r.p.m. of each independent wheel. The steady-state error \(E_{ss}\) is minimized during the deceleration via the proposed FLC by driving the motor(s) in the opposite direction. However, this could be more effective by applying brake force during the deceleration via a fuzzy logic control magnetic brake mechanism [109; 110] in addition to a reverse polarity changing mechanism. The greatest average r.p.m. and  
Figure 13: The throttle signal is given to the controller to achieve the desired translational velocity. The neutral position of the steering angle level is 1.7 (to achieve a zero-yaw angle) [104]. translational velocity values of the rover are 1024.4 r.p.m. and 25.09 km/h, respectively, at the maximum r.p.m. Figures 16 and 17 show the three orthogonal acceleration directions (\"_AccX: longitudinal_\", \"_AccY: lateral_\" and \"_AccZ: radial_\") and three gyro-angles (\"_GyYaw: Yaw_\", \"_GyPit: Pitch_\" and \"_GyRol: Roll_\") observed when the wheel speed data were examined. Furthermore, Figure 16 demonstrates that as the rover achieved a high r.p.m., the radial acceleration varied dramatically. This occurred as a result of an uneven road surface. Figure 17 shows that the rover encountered a slope of around 30 degrees within about 3.5 s. Again, within 3.5 s, the rover's actual yaw angle was pushed away from its target yaw angle by roughly 10 degrees. The yaw angle variation was 10 degrees in relation to the beginning location when compared to the overall travel distance (measured distance was 105.64 m). Furthermore, due to the slope of the grass surface into the lateral direction of the rover, a roll angle of roughly 2 degrees occurred. Based on the observed wheel speed data, the wheel slip ratio, \(S_{j}\), was estimated in real time for various road surfaces using Equation (3) in [104] (for different friction coefficients).