A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators

作者：Khoukhi Amar^*; Baron Luc; Balazinski Marek; Demirli Kudret

来源：Engineering Applications of Artificial Intelligence, 2008, 21(7): 974-984.

DOI：10.1016/j.engappai.2007.12.004

• Robot manipulators
• Multi-objective trajectory planning
• Minimum-time control
• Hierarchical neuro-fuzzy systems

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 arc learned and optimized, generating the optimal-time trajectory torques, representing the robot dynamic behavior. Simulation results are presented and discussed.

• 出版日期2008-10
• 单位中国石油大学（北京）