Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

• Published in: Expert systems with applications 2012-12, Vol.39 (18), p.13295-13304
• Main Authors: Petković, Dalibor, Issa, Mirna, Pavlović, Nenad D., Zentner, Lena, Ćojbašić, Žarko
• Format: Article
• Language: English
• Subjects: Adaptive compliant gripper; Adaptive control systems; ANFIS controller; Computer simulation; Controllers; Embedded sensors; Fuzzy; Fuzzy control; Fuzzy logic; Grippers; Object recognition; Object recognizing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.05.072

► Controlling input displacement of a new adaptive compliant gripper. ► This design of the gripper with embedded sensors as part of its structure. ► A new and original principle for adaptive grasping. ► The handling of irregular, unpredictably shaped and sensitive objects. 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 то 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.