Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

  • Authors:

  • Dalibor Petković;Mirna Issa;Nenad D. Pavlović;Lena Zentner;Arko OjbašIć

  • Affiliations:

  • University of Niš, Faculty of Mechanical Engineering, Department of Mechatronics, Aleksandra Medvedeva 14, 18000 Niš, Serbia;Ilmenau University of Technology, Faculty of Mechanical Engineering, Department of Mechanism Technology, P.O. Box 100565, 98684 Ilmenau, Germany;University of Niš, Faculty of Mechanical Engineering, Department of Mechatronics, Aleksandra Medvedeva 14, 18000 Niš, Serbia;Ilmenau University of Technology, Faculty of Mechanical Engineering, Department of Mechanism Technology, P.O. Box 100565, 98684 Ilmenau, Germany;University of Niš, Faculty of Mechanical Engineering, Department of Mechatronics, Aleksandra Medvedeva 14, 18000 Niš, Serbia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult @?@? control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.