Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

Indirect adaptive fuzzy control for industrial robots: A solution for contact applications

“…This approach was successfully applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system. Adaptive neuro fuzzy inference strategies was used to control input displacement of a new adaptive compliant gripper (Petković, Issa, Pavlović, Zentner, & Ćojbašić, 2012;Petković, Pavlović, Ćojbašić, & Pavlović, 2013). An adaptive charged system search (ACSS) algorithm for the optimal tuning of TakagiSugeno proportional-integral fuzzy controllers is proposed for the position control of a nonlinear servo system (Precup, David, Petriu, Preitl, & Rădac, 2014).…”

Evaluation of the most influential parameters of heat load in district heating systems

“…ANFIS [32], a hybrid intelligent system that increases the capability of learning and adapting automatically, has been used by researchers for many different purposes in a variety of engineering systems, such as modeling [33][34][35][36], prediction [37][38][39], and control [40][41][42][43][44]. The M a n u s c r i p t 6 ANFIS methodology aims to organize the FIS (fuzzy inference system) by analyzing the input/output data pairs [45,46].…”

Analysis of the optimal orientation of robot gripper for an improved capability assembly process

“…In other works, Petković et al [35,36] predicted of grasping object weight for a passively compliant gripper and determined the joints that were most strained in an underactuated robotic finger using an adaptive neuro-fuzzy methodology. An adaptive neuro-fuzzy network is also used in ANFIS to approximate the correlation between contact point locations and contact forces magnitudes [37,38] and to estimate the conductive rubber mechanical properties. The ANFIS system is capable of finding any change in the ratio of the positions of the gripper contacts and magnitudes of the contact forces.…”