Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator-Reference-Cited by-同舟云学术

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Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator

Published:2024-02 Issue: Volume:152 Page:110756
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Viswanadhapalli Joshi Kumar^ORCID,Elumalai Vinodh Kumar^ORCID,S. Shivram,Shah Sweta,Mahajan Dhruv

Publisher

Elsevier BV

Subject

Software

Reference44 articles.

1. Passivity-based control of single-link flexible manipulators using a linear strain feedback;Feliu;Mech. Mach. Theory,2014

2. An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study;Silva;Control Eng. Pract.,2020

3. Integral resonant control for vibration damping and precise tip-positioning of a single-link flexible manipulator;Pereira;IEEE/ASME Trans. Mechatronics,2011

4. Flexible-link robots with combined trajectory tracking and vibration control;Garcia-Perez;Appl. Math. Model.,2019

5. Dynamic model and robust control of flexible link robot manipulator;Khairudin;Telkomnika,2011

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3. Fuzzy PI vibration suppression control strategy for space double flexible telescopic manipulator with fractional disturbance observer;Aerospace Science and Technology;2024-09

4. Modeling and analysis of rigid-flexible coupling dynamics of a cable-driven manipulator;Journal of Mechanical Science and Technology;2024-08

5. An Intelligent Fault-Tolerant Control Method for a Flexible-Link Manipulator with an Uncertain Dead-Zone and Intermittent Actuator Faults;Mathematics;2024-07-11

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