**8. Experimental results**

106 Fuzzy Inference System – Theory and Applications

Fig. 24. System response for the second scheme controller Mass = 300 gm and *Fpush* =150 g

(b) Mass=100 gm,

force

=0.5, and *Fpush* =40 gm-

.

(a) Mass=100 gm,

gm-force

Fig. 25. Slippage parameters and applied force.

=0.5, and *Fpush* =20

Experimental work was established to verify the gripper system performance. Every part of the system was verified from the design concept, the manufacturing and control aspects. The mechanical system performance was tested and suitable refinements were performed. Sensors were calibrated and their necessary drive circuits were built. The actuator characteristics were studied in order to be taken into consideration during grasping process. Figure 26 shows the flowchart that describes the experimental scenario and proposed algorithm. Figures 27 and 28 show the system response during grasping and lifting for 1000gm object mass. Figures 27(a) and 28(a) show good performance although the start reference controller based on pushing

Fig. 26. Flow chart of the proposed scenario.

Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System 109

Ch4: Slip-rate (mm/s) – Ch3: Fapp (gm-force) – Ch1: Vref (V) (a) Pushing force is not considered

Ch4: Slip-rate (m/s) – Ch3: Fapp (gm-force) – Ch1: Vref (V) (b) Pushing force is considered

force is not considered. The duration of the grasping and lifting process was in the range of

of the developed gripper set-up and its control, we disturb the assigned system by a sudden increase in object mass. The gripper system response was found as shown in Fig.29, which keeps the time of slippage and slip displacement in the range of 1 second and 2 millimeters

Fig. 28. System response when mass=1000gm

1 second and the slip displacement is in the range of 2 millimeters.

Considering the start reference controller based on pushing force as shown in Figure.27 (b) and in Figure. 28 (b), we can minimize the time of the grasping and lifting process. Moreover, a slip displacement reduction was achieved. To confirm and verify the robustness

(b)Pushing force is considered

Fig. 27. System response when mass=550gm

Considering the start reference controller based on pushing force as shown in Figure.27 (b) and in Figure. 28 (b), we can minimize the time of the grasping and lifting process. Moreover, a slip displacement reduction was achieved. To confirm and verify the robustness

> Ch4: Slip-rate (mm/s) – Ch3: Fapp (gm-force) – Ch1: Vref (V) (a) Pushing force is not considered

> Ch4: Slip-rate (mm/s) – Ch3: Fapp (gm-force) – Ch1: Vref (V) (b)Pushing force is considered

Fig. 27. System response when mass=550gm

force is not considered. The duration of the grasping and lifting process was in the range of 1 second and the slip displacement is in the range of 2 millimeters.

of the developed gripper set-up and its control, we disturb the assigned system by a sudden increase in object mass. The gripper system response was found as shown in Fig.29, which keeps the time of slippage and slip displacement in the range of 1 second and 2 millimeters

Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System 111

is not considered. It is clear from the table that the performance of the system in the case of the second controller scheme is better than in the case of the first controller. The duration of the process is lower in the second scheme and also the amount of the slip is reduced for all test cases where the mass of the object is varying between 100g and 1000g. This proves that the feedback variables choice is very important and has a great effect on the system

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respectively. In the mean time Table 1 shows a comparison between the two proposed schemes. The enhancement in the response when the pushing force is considered gives us the opportunity to grasp safely objects with higher mass than in the first scheme where Fpush

Ch 4:slip rate(mm/s) – Ch 3: Fapp(gm-force) – Ch1: Vref(V)

Fig. 29. System response when mass is suddenly increased from 550 to 900gm


is not considered. It is clear from the table that the performance of the system in the case of the second controller scheme is better than in the case of the first controller. The duration of the process is lower in the second scheme and also the amount of the slip is reduced for all test cases where the mass of the object is varying between 100g and 1000g. This proves that the feedback variables choice is very important and has a great effect on the system performance.
