کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
381695 | 1437494 | 2008 | 11 صفحه PDF | دانلود رایگان |
In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-of-freedom planar manipulator using a hierarchical hybrid neuro-fuzzy system. A first neuro-fuzzy network named NeFIK is considered to solve the inverse kinematics problem. After a few pre-processing steps characterizing the minimum-time trajectory and the corresponding torques, a second neuro-fuzzy controller is built. Its purpose is to fit the robot dynamic behavior corresponding to the determined minimum-time trajectory with respect to actuators models, torque nominal values, as well as position, velocity, acceleration and jerk boundary conditions. A Tsukamoto Neuro-Fuzzy Inference network is designed to achieve the online control of the robot. The premise parameters (antecedent membership functions parameters) as well as rule-consequence parameters are learned and optimized, generating the optimal-time trajectory torques, representing the robot dynamic behavior. Simulation results are presented and discussed.
Journal: Engineering Applications of Artificial Intelligence - Volume 21, Issue 7, October 2008, Pages 974–984