Model-Adaptive Hybrid Dynamic Control for Robotic Assembly Tasks

Author:

Austin David J.1,McCarragher Brenan J.2

Affiliation:

1. Center for Autonomous Systems, Royal Institute of Technology, SE 100-44 Stockholm, Sweden

2. Department of Engineering, Faculties, Australian National University, Canberra, ACT 0200, Australia

Abstract

A new task-level adaptive controller is presented for the hybrid dynamic control of robotic assembly tasks. Using a hybrid dynamic model of the assembly task, velocity constraints are derived from which satisfactory velocity commands are obtained. Due to modeling errors and parametric uncertainties, the velocity commands may be erroneous and may result in suboptimal performance. Task-level adaptive control schemes, based on the occurrence of discrete events, are used to change the model parameters from which the velocity commands are determined. Two adaptive schemes are presented: the first is based on intuitive reasoning about the vector spaces involved whereas the second uses a search region that is reduced with each iteration. For the first adaptation law, asymptotic convergence to the correct model parameters is proven except for one case. This weakness motivated the development of the second adaptation law, for which asymptotic convergence is proven in all cases. Automated control of a peg-in-hole assembly task is given as an example, and simulations and experiments for this task are presented. These results demonstrate the success of the method and also indicate properties for rapid convergence.

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

Reference7 articles.

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hybrid Vision/Force Control of Robotic Autonomous Assembly for 110kV Hot-line Maintenance;2021 China Automation Congress (CAC);2021-10-22

2. Human error identification in programming by demonstration of compliant motion robotic tasks;2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2013-07

3. T-S fuzzy contact state recognition for compliant motion robotic tasks using gravitational search-based clustering algorithm;2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2013-07

4. Motion Planning of Bimanual Robot Using Adaptive Model of Assembly;IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences;2008-12-01

5. Error prevention in robotic assembly tasks by a machine vision and statistical pattern recognition method;International Journal of Production Research;2005-04

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