Novel hybrid adaptive controller for manipulation in complex perturbation environments

Alex M C Smith; Chenguang Yang; Hongbin Ma; Phil Culverhouse; Angelo Cangelosi; Etienne Burdet

doi:10.1371/journal.pone.0129281

Novel hybrid adaptive controller for manipulation in complex perturbation environments

PLoS One. 2015 Jun 1;10(6):e0129281. doi: 10.1371/journal.pone.0129281. eCollection 2015.

Authors

Alex M C Smith¹, Chenguang Yang², Hongbin Ma³, Phil Culverhouse¹, Angelo Cangelosi¹, Etienne Burdet⁴

Affiliations

¹ Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK.
² Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK; College of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
³ State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, China; School of Automation, Beijing Institute of Technology, Beijing, China.
⁴ Department of Bioengineering, Imperial College London, London, UK.

Abstract

In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation*
Environment*
Fuzzy Logic*
Humans
Learning
Models, Theoretical*
Robotics / instrumentation*
Robotics / methods

Grants and funding

The authors would like to acknowledge support from Engineering and Physical Sciences Research Council (EPSRC) grants EP/L026856/1 and EP/J004561/1 BABEL, National Natural Science Foundation of China (NSFC) grants 61473120 and 61473038, Guangdong Provincial Natural Science Foundation of China grant 2014A030313266, and European Commission grant FP7-ICT-601003 BALANCE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.