This paper presents an iterative learning algorithm for functional approximation, with applications to the robot kinematics problems. Various approaches have been proposed in the literature to approximate the kinematic models of robots. However, most of them assume that either the kinematic parameters or the kinematic structures of the robots are known. Neural network (NN) has been known for its inherent functional approximation capability and can be used to approximate the models when the structures of the robots are unknown. Most of these NN methods are formulated as gradient-based learning algorithms and there is no theoretical analysis to ensure convergence. Our proposed method in this paper does not require any computation of the gradient of the cost function or the inverse matrix. The convergence of the algorithm is guaranteed by theoretical analysis. The performance of the algorithm is illustrated by using a radial basis function (RBF) neural network to approximate the kinematic models of two different robots.
|Title of host publication||Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2019 Jul|
|Event||2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China|
Duration: 2019 Jul 8 → 2019 Jul 12
|Name||IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM|
|Conference||2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019|
|Period||19/7/8 → 19/7/12|
Bibliographical noteFunding Information:
*This work was supported by the Agency For Science, Technology and Research of Singapore (A*STAR), under the AME Individual Research Grants 2017 (Proposal#17283084; Project# A1883c0008).
This work was supported by the Agency For Science, Technology and Research of Singapore (ASTAR), under the AME Individual Research Grants 2017 (Proposal#17283084; Project# A1883c0008)
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications