Development of a new EDRNN procedure in control of human arm trajectories

Liu, Shan; Wang, Yongji; Zhu, Quanmin

Abstract

In this paper the trajectory tracking control of a human arm moving on the sagittal plane is investigated by an interdisciplinary approach with the combination of neural network mapping, evolutionary computation, and dynamic system control. The arm in the study is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in the muscle space. A new control system structure is proposed to manipulate the complicated nonlinear dynamical arm motion. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, in which genetic algorithm (GA) and evolutionary program (EP) strategy are effectively integrated with the diagonal recurrent neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN architecture and an adaptive dynamic back-propagation (ADBP) algorithm with momentum for the multi-input multi-output (MIMO) systems is used to obtain the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study. © 2007 Elsevier B.V. All rights reserved.

• Journal Article Type: Article
• Publication Date: Dec 1, 2008
• Deposit Date: Dec 20, 2010
• Publicly Available Date: Feb 18, 2016
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 72
• Issue: 1-3
• Pages: 490-499
• DOI: https://doi.org/10.1016/j.neucom.2007.12.012
• Keywords: evolutionary diagonal recurrent neural network, genetic algorithm, evolutionary program, adaptive dynamic back-propagation algorithm, trajectory tracking control, musculoskeletal model
• Public URL: https://uwe-repository.worktribe.com/output/1022979
• Publisher URL: http://dx.doi.org/10.1016/j.neucom.2007.12.012
• Contract Date: Feb 18, 2016