Guest Editorial Introduction to the Focused Section on Adaptive Learning and Control for Advanced Mechatronics Systems

Huijun Gao, Bin Liang, Roberto Oboe, Yang Shi, Sen Wang, Masayoshi Tomizuka

Research output: Contribution to journalEditorialpeer-review

Abstract

Mechatronics, which cooperate with or substitute human operators to perform a growing variety of tasks, are getting increasingly complex in order to implement difficult operations with comprehensive utilization of sensors, vision modules, actuators, controllers, etc., and the intelligent algorithms with learning capability running behind. Nowadays, mechatronics task in a wide range of fields requiring intelligent and flexible actions in unstructured/fast-changing working environments, which brought great challenges to decision, planning and control of advanced mechatronics systems. In recent years, the adaptive and learning-based methods have shown their vitality on perception, recognition, situation understanding, communication and trajectory planning, etc., with a great amount of proved successful applications in many cross disciplines [A1]–[A3].
Original languageEnglish
Pages (from-to)607-610
Number of pages4
JournalIEEE/ASME Transactions on Mechatronics
Volume27
Issue number2
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Adaptive learning
  • IEEE transactions
  • Mechanical engineering
  • Mechatronics
  • Motion planning
  • Robots
  • Task analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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