Backlash compensation for a humanoid robot using disturbance observer

Jung, Byung-Jae; Kong, Jung-Shik; Lee, Bo-Hee; Ahn, Sang-Min; Kim, Jin-Geol

doi:10.1109/iecon.2004.1432128

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…To overcome this nonlinearity, the backlash compensator is required. Now, considering the nonlinear system for the backlash is desired in Brunovsky form as (Byung-Jae et al, 2004;Kang et al, 2011):…”

Section: Backlashmentioning

confidence: 99%

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Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Jamil

Kongprawechnon

Raza

2016

Aircraft Eng & Aerospace Tech

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2016),"A conservative sliding mesh coupling procedure for U-RANS flow simulations", Aircraft Engineering and Aerospace Technology, Vol. 88 Iss 1 pp. 151-158 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of the proposed research methodology is to control the trajectory tracking of EDRM and also to cancel out the effect of no-smooth nonlinearities, which affect the system performance badly. Design/methodology/approach -Robust adaptive neural network (RANN)-based backstepping control design methodology is presented in this paper. The proposed design methodology improves the trajectory tracking and running mean error. Findings -The running mean error results show that the convergence of the proposed RANN-based backstepping technique is very fast as compare to the conventional PD control and due to this proposed control technique, the EDRM follows its desired trajectory perfectly. Practical implications -The EDRM trajectory tracking performance increases which leads to a better working position of EDRM. Originality/value -The originality of this research article is 93 per cent. Backstepping control designMuhammad Usman Jamil et al.

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Section: Backlashmentioning

confidence: 99%

“…Now, considering the nonlinear system for the backlash is desired in Brunovsky form as (Byung-Jae et al, 2004;Kang et al, 2011):…”

Section: Backlashmentioning

confidence: 99%

Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Jamil

Kongprawechnon

Raza

2016

Aircraft Eng & Aerospace Tech

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Implementation of an Intelligent Controller for Biped Walking Robot using Genetic Algorithm

Kho

Lim

Kuc

2006

2006 IEEE International Symposium on Industrial Electronics

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This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robot's motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.

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Contact detection and physical interaction for low cost personal robots

Flacco¹,

Kheddar

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Abstract-We present a methodology for estimating joints torque due to external forces applied to a robot with large joints backlash and friction. This undesired non-linearity is common in personal robot, due to the use of low cost mechanical components and type of usage. Our method enables contact detection and human-robot physical interaction capabilities without using extra sensors. The effectiveness of our approach is shown with experiments on a Romeo robot arm from SoftBank Robotics.

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Backlash compensation for a humanoid robot using disturbance observer

Cited by 5 publications

References 8 publications

Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Implementation of an Intelligent Controller for Biped Walking Robot using Genetic Algorithm

Contact detection and physical interaction for low cost personal robots

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