Journal article
Multitask neuromorphic controller for redundant robots
Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes, v 2, pp 1885-1890
01 Jan 1994
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this article, we propose a multitask neuromorphic controller with a hierarchical architecture, which consists of two artificial neural network (ANN) sub-systems. Based on Hopfield model, the higher level neural network system is designed to solve kinematics problems for redundant robots with several constraints in an environment of collision-free. The lower neural network system at servolevel, built on Backpropagation (BP) algorithm, is employed to control joints of the manipulator with approximate dynamic model to track the reference trajectory accurately. The stability characteristics of the subcontroller and the convergence property of the ANNs are mathematically analyzed. Furthermore, improvements on learning of the proposed ANNs are also addressed in this paper.
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Details
- Title
- Multitask neuromorphic controller for redundant robots
- Creators
- Bin Jin - Drexel UniversityAllon Guez - Drexel University
- Publication Details
- Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes, v 2, pp 1885-1890
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1994BC17X00408
- Other Identifier
- 991019182665104721
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- Web of Science research areas
- Automation & Control Systems
- Engineering, Industrial
- Operations Research & Management Science