z-logo
open-access-imgOpen Access
Recursive Differential Evolution Algorithm for Inertia Parameter Identification of Space Manipulator
Author(s) -
Zhengxiong Liu,
Panfeng Huang,
Zhenyu Lu
Publication year - 2016
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/63935
Subject(s) - inertia , computer science , algorithm , inertial frame of reference , recursive least squares filter , nonlinear system , convergence (economics) , trace (psycholinguistics) , identification (biology) , control theory (sociology) , least squares function approximation , differential (mechanical device) , mathematics , artificial intelligence , control (management) , philosophy , estimator , aerospace engineering , economic growth , linguistics , adaptive filter , engineering , biology , classical mechanics , quantum mechanics , statistics , physics , botany , economics
This paper proposes a recursive differential evolution (RDE) algorithm to identify the inertial parameters of an unknown target and simultaneously revise the friction parameters of space manipulator joints. The inertia parameters of a space manipulator, which govern the dynamic behaviours of the entire system to a significant extent, can change for many reasons during the process of on-orbit operations; consequently, it is essential to trace these changes within the control system to ensure the stability and accuracy of the entire system. RDE is inspired by a recursive least squares algorithm, using approximate gradient information to guide the mutation operation in the standard DE. A series of contrast simulations are employed to confirm the feasibility of the RDE algorithm. The simulation results show that the identification of the RDE algorithm is more precise than for a GA (genetic algorithm) and LS (least square) algorithm, and has an appropriate convergence rate. The RDE identification method is suitable for linear, nonlinear and combined systems, and can follow system dynamics exactly

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom