Recurrent-Neural-Network-Based Velocity-Level Redundancy Resolution for Manipulators Subject to a Joint Acceleration Limit

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 5; pp. 3573 - 3582
• Main Authors: Zhang, Yinyan, Li, Shuai, Zhou, Xuefeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Computer simulation; Joint limits; kinematic control; Kinematics; manipulator; Manipulator dynamics; Manipulators; neural network; Neural networks; Optimization; Recurrent neural networks; Redundancy; redundancy resolution; Velocity
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2018.2851960

For the safe operation of redundant manipulators, physical constraints such as the joint angle, joint velocity, and joint acceleration limits should be taken into account when designing redundancy resolution schemes. Velocity-level redundancy resolution schemes are widely adopted in the kinematic control of redundant manipulators due to the existence of the well-tuned inner loop regarding the joint velocity control. However, it is difficult to deal with joint acceleration limits for velocity-level redundancy resolution methods. In this paper, a recurrent-neural-network-based velocity-level redundancy resolution method is proposed to deal with the problem, and theoretical results are given to guarantee its performance. By the proposed method, the end-effector position error is asymptotically convergent to zero, and all the joint limits are not violated. The effectiveness and superiority of the proposed scheme are validated via simulation results.