An overview of dynamic parameter identification of robots

Wu, Jun; Wang, Jinsong; You, Zheng

doi:10.1016/j.rcim.2010.03.013

Cited by 330 publications

(184 citation statements)

References 33 publications

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“…Often, only a linear combination of some parameters is observable, rendering these parameters partially observable. By optimizing the joint trajectories [9], the number of observable parameters can be maximized.…”

Section: A Observabilitymentioning

confidence: 99%

“…However, the drawback of these methods is that they rely on the accuracy of the robot dynamic model [1], [6], whose parameters are usually calculated using computeraided design (CAD) software [1], [7]- [9]. Although CAD softwares can calculate these parameters accurately, additional elements like hoses and cables are usually not taken into account [1].…”

Section: Introductionmentioning

confidence: 99%

“…Although CAD softwares can calculate these parameters accurately, additional elements like hoses and cables are usually not taken into account [1]. Moreover, the density set for the materials can differ from reality [9], causing significant errors. Inaccurate parameters result in a wrong prediction of the forces, moving the control burden toward the feedback controller and thus increasing tracking errors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online payload identification for quadruped robots

Tournois

Focchi

Prete³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The identification of inertial parameters is crucial to achieve high-performance model-based control of legged robots. The inertial parameters of the legs are typically not altered during expeditions and therefore are best identified offline. On the other hand, the trunk parameters depend on the modules mounted on the robot, like a motor to provide the hydraulic power, or different sets of cameras for perception. This motivates the use of recursive approaches to identify online mass and the position of the Center of Mass (CoM) of the robot trunk, when a payload change occurs. We propose two such approaches and analyze their robustness in simulation. Furthermore, experimental trials on our 80-kg quadruped robot HyQ show the applicability of our strategies during locomotion to cope with large payload changes that would otherwise severely compromise the balance of the robot.

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Section: A Observabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online payload identification for quadruped robots

Tournois

Focchi

Prete³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In Wu et al (2010), the authors provide an overview of the existing work on dynamic parameter identification methods. There are two main methods for parameter identifications: online and off-line parameter estimation.…”

Section: Model Parameter Estimationmentioning

confidence: 99%

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Gale¹,

Rahmati²,

Gravdahl³

et al. 2017

Front. Robot. AI

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A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS). Different crossvalidation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.

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“…Traditional model-based strategies are often applied dealing with imprecise model identification or measurement of inertial and friction parameters [1], [2], [3], [4]. But, the identification or measurement of parameters is a demanding task, with difficulties on excitation functions, validation of results [5], [6], [7], [8] and possible unexpected performance of the plant [5]. Due to the increasing complexity of robot arm applications like lifting or lowering objects, picking up objects from shelves or helping people with personal care activities, careful evaluation of control performance in novel approaches becomes an issue of interest, considering the analysis of the ability to handle payloads in common daily situations due to sudden changes or unstructured environments [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Unfalsified adaptive control for manipulators with parameter uncertainties

Arango

Ocampo‐Martínez

Bianchi

et al. 2015

2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC)

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Abstract-This work evaluates by simulation the performance of the Unfalsified Adaptive Control (UAC) for Multiple Degree of Freedom (MDoF) serial manipulators. The UAC is a data-driven technique that addresses stability issues of model-based controllers for robot arms with inertial uncertainties. The unfalsified controller selects the most suitable controller from a set, based on performance, to decide whether the controller in the closed loop should be changed, using only system inputs and outputs, i.e., torques and joint variables of the robotic arm, respectively. In this work, performance and robustness is evaluated by simulation on a 5-DoF manipulator showing the ability of the UAC to accomplish tracking tasks in the presence of inertial parameters disturbances.

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An overview of dynamic parameter identification of robots

Cited by 330 publications

References 33 publications

Online payload identification for quadruped robots

Online payload identification for quadruped robots

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Unfalsified adaptive control for manipulators with parameter uncertainties

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