Dynamic identification of Staubli RX-60 robot using PSO and LS methods

Bingül, Zafer; Karahan, Oğuzhan

doi:10.1016/j.eswa.2010.09.076

Cited by 60 publications

(40 citation statements)

References 30 publications

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“…Among these works, some features of mass distribution were assumed in order to simplify the analysis and get an approximate-simplified model. Examples of papers that include experimental results using a three-degree-of-freedom robot are [12,13,15,21,22]. In the case of our experimental robot, the model was developed without assuming any shapes in the links, i.e., the links are modelled as a general rigid body, as a result a complete dynamic model was obtained.…”

Section: Reyes and Kellymentioning

confidence: 99%

“…Using the property of linearity in the parameters of the robot's total energy [5] the principle of the conservation of energy (15) and assuming that the total energy at the instant zero is null, we obtain the supplied-energy regression model [4]: (16) which is linear in the barycentric and friction parameters. Note that, in order to compute the regressor matrix, the measurement of joint acceleration  q is not required.…”

Section: Supplied-energy Regression Modelmentioning

confidence: 99%

“…They classify methods as off-line and on-line and also discuss trajectory optimization. In [15] we can find a comparison between two methods to estimate only the inertial parameters of an industrial robot: the least squares method and particle swarm optimization. They conclude that the accuracy of the estimated parameters depends on the estimation methods, the measurement accuracy and the selection of efficient exciting trajectories.…”

Section: Reyes and Kellymentioning

confidence: 99%

See 2 more Smart Citations

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Chávez-Olivares

Reyes-Cortés

González-Galván

et al. 2012

International Journal of Advanced Robotic Systems

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The inertial and friction parameters of a robot are used in the development and evaluation of modelbased control schemes and their accuracy is related directly to the performance. These parameters can also be used for a realistic simulation, which may be useful before implementation of new control schemes. In principle, the numerical value of the parameters could be obtained via CAD analysis, but inevitably assembly and manufacturing errors exist. Direct measurement is not a realistic option because the complex nature of the system involves intense, time-consuming effort. Alternatively, we can deduce the values of the parameters by observing the natural response of the system under appropriate experimental conditions, i.e., by using identification schemes, which is an efficient way. This paper presents the experimental evaluation of five identification schemes used to obtain the inertial and friction parameters of a three-degrees-of-freedom direct-drive robot. We assume that the inertial and friction parameters are totally unknown but, by design, the dynamic model is fully known, as in many practical cases. We consider the schemes based on the dynamic regression model, the filtered-dynamic regression model, the supplied-energy regression model, the power regression model and the filtered-power regression model. This paper presents a comparison between experimental and simulated robot response, which enables us to verify the accuracy of each regression model.

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Section: Reyes and Kellymentioning

confidence: 99%

Section: Supplied-energy Regression Modelmentioning

confidence: 99%

Section: Reyes and Kellymentioning

confidence: 99%

See 1 more Smart Citation

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Chávez-Olivares

Reyes-Cortés

González-Galván

et al. 2012

International Journal of Advanced Robotic Systems

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show abstract

“…Due to its simple arithmetic, fewer parameters, easy to implement, and good optimizing capacity, PSO shows a good performance in continuous optimization problem and discrete optimization problem, and it has been a research hotspot of intelligent optimization field [3]. Bingül [4] identified the inertial parameters of a RX-60 robot by PSO, and compared the performance of PSO and least square method. Wang [5] used PSO to determine the parameters of spindle-holder and holder-tool interfaces.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of a Shell Transfer Arm Using PSO and Similarity Degree Analysis

Zhao¹,

Hou²

2016

IJMO

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Abstract-A shell transfer arm is a complex mechanical electro-hydraulic system with multiple parameters. An accurate dynamic model is the precondition of improved design. However, during the modeling process, several parameters that have a significant effect on the performance of the system can be achieved only by identification methods because they are hard to measure. According to the structure and control theory of the shell transfer arm, the analytical model and control model are built. To evaluate the level of similarity of the built model and real system, the numerical similarity and shape similarity of the angular velocity of the arm are calculated, and the combination similarity of numerical similarity and shape similarity is set as the fitness function of Particle Swarm Optimization (PSO). Four unknown parameters are identified by PSO. The identification results with respect to the simulation data show that the identification accuracy of PSO can acquires the requirement. And the identification results with respect to the test data show that the built model is accurate, and parameter identification of the shell transfer arm based on PSO and similarity degree analysis of time-vary data is feasible and effective.Index Terms-Shell transfer arm, dynamic modeling, parameter identification, particle swarm optimization, similarity degree analysis.

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“…Recently, some novel dynamical identification methods for robot manipulators have been reported using intelligence algorithms. For instance, Bingül and Karahan [8] integrated the particle swarm optimization (PSO) algorithm with LS algorithm to estimate the dynamical parameters of Staubli RX-60 robots. In the identification experiment, the velocity and acceleration are measured by three high-speed cameras, and the joint torques are measured by six load cell sensors.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

Ding

et al. 2018

Journal of Robotics

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This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

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Dynamic identification of Staubli RX-60 robot using PSO and LS methods

Cited by 60 publications

References 30 publications

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Parameter Identification of a Shell Transfer Arm Using PSO and Similarity Degree Analysis

Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

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