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Generically Dependent Continuant
RDF description of Experimental evaluation of parameter identification schemes on an anthropomorphic direct drive robot - http://orbis.uaslp.mx/vivo/individual/scopus3223
International Journal of Advanced Robotic Systems
Direct Drive Robot; Identification Schemes; Least-Squares Algorithm; Regression Models Complex nature; Control schemes; Direct measurement; Direct-drive robot; Dynamic regression models; Experimental conditions; Experimental evaluation; Friction parameters; Identification scheme; Least-squares algorithms; Manufacturing errors; Model-based control schemes; Natural response; Numerical values; Realistic simulation; Regression model; Simulated robot; Anthropomorphic robots; Friction; Regression analysis; Tribology; Parameter estimation
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. © 2012 Chávez-Olivares et al.
Experimental evaluation of parameter identification schemes on an anthropomorphic direct drive robot
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