14960

13464

كارشناسي ارشد

كنترل

اصفهان : دانشگاه صنعتي اصفهان

1398

يازده، 82ص. : مصور، جدول، نمودار

مريم ذكري، مرضيه كمالي انداني

محمد دانش

فريد شيخ الاسلام، مجدالدين نجفي

1398/04/30

كتابنامه

مهندسي برق

مهندسي برق و كامپيوتر

1398/06/04

2549614

Constrained Nonlinear Control of SCARA Using Adaptive IQ PD Controller Najmeh Eskandari najmeh eskandariy 90@gmail com June 24 2019 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisors Prof Maryam Zekri mzekri@cc iut ac ir Prof Marzieh Kamali mkamali@cc iut ac ir Abstract Recently the utilization of robots in the industry has faced a dramatic growth In not to distance future we will observe awidespread implementation of them in household and medical applications In the meantime the safety issue is vital becauseof the robot s interaction with humans Impedance control as one of the flexible methods against external forces has beenable to prevent damages during a collection to a enviromental obstacle In this method the robot is taken into account asa mass spring damper system In the impedance control force and position control are not accomplished separately butthe system s error dynamics or in other words the relationship between external forces and tracking error should follow adesirable dynamic In the first phase the design of the impedance controller for the Scara robot as the nominal controllerand the the main target of the controlling aim has been addressed while flexing against external forces Invariance control isemployed as one of the control methods to apply constraints in dynamic enviroments This controller prevents the violationof the limitations or or predetermined vicinity limit to the vulnerable obstacle by switching between nominal and correctivecontrol Hence in the next phase an invariant controller is designed as a system coorecting controller for the behavior ofthe system and the generator of the optimal secondary path The Q learning algorithm is one of the methods of makingcontrol systems intelligent This algorithm which is subset of reinforcement learning algorithms attempts to learn optimalbehavior using trial and error based on variant enviroment conditions Since the state of states and actions in the algorithmare discrete in step three by discretization of these two spaces for the Scara robot and adjusting the coefficients of the PDcontrollers deploying the online incremental Q learning algorithm the robot is well controlled Key Words Manipulators Impedance Control Invariance Control Incremental Q Learning

مريم ذكري، مرضيه كمالي انداني

محمد دانش

فريد شيخ الاسلام، مجدالدين نجفي