Title :
Multi-Input Fuzzy Rules Emulated Networks with a Hertzian Contact Force Sensor
Author :
Armendariz, J. ; Treesatayapun, C. ; Baltazar, A.
Author_Institution :
Robot. & Adv. Manuf. Program, CINVESTAV-IPN, Saltillo, Mexico
fDate :
Nov. 26 2011-Dec. 4 2011
Abstract :
In this paper, the implementation of an adaptive neuro-fuzzy network called Multi-Input Fuzzy Rules Emulated Network (MIFREN) is presented. This network has a simple structure to describe the human knowledge about the robotic manipulator and the sensor in the form of fuzzy IF-THEN rules. MIFREN is implemented as a force feedback controller which used a force sensor based on Hertzian contact and ultrasound. The contact probe exhibits high sensitivity to initial instantaneous contact and provides an estimation of the reaction force. Based on the force signal provided by this probe, the MIFREN´s structure can be constructed and integrated directly in the controller without any mathematical model of the system. The experimental results with synthetic elastomer and tomato samples demonstrate the superior performance of MIFREN compared to a well tuned PI controller.
Keywords :
adaptive control; elastomers; force feedback; force sensors; fuzzy control; manipulators; neurocontrollers; Hertzian contact force sensor; MIFREN; adaptive neurofuzzy network; force estimation; force feedback controller; fuzzy IF-THEN rules; multi-input fuzzy rule emulated networks; robotic manipulator; synthetic elastomer; ultrasound; Force; Force feedback; Manipulators; Probes; Robot sensing systems; Ultrasonic imaging; Neuro-fuzzy control; force control; force sensing; high sensitivity; robotic manipulator; ultrasound;
Conference_Titel :
Artificial Intelligence (MICAI), 2011 10th Mexican International Conference on
Conference_Location :
Puebla
Print_ISBN :
978-1-4577-2173-1
DOI :
10.1109/MICAI.2011.17