Abstract
This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.
Highlights
Direct and cross-validations are performed, and the results are discussed, with some criteria of performance metrics analyzed to show the role of the soft sensor-based differentiator used in the parametric identification loop
Our aim is to compare the practical parameter estimation by the proposed differentiator P2SMD with the estimations by the classic sliding mode differentiator defined in Equation (4) (2SMD), the basic Euler differentiator associated with the FIR low-pass filter (ED+FIR), and a new scheme of the adaptive super twisting differentiator (ASTD) proposed by Shtessel [30]
The results indicate a significant difference between the three studied algorithms (ED+FIR, 2SMD, and ASTD) and the P2SMD algorithm
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The general problem of manipulators still lies in the large number of their physical parameters, which are usually not well known. To correctly build a mathematical model of such systems, different parameter identification techniques exist, which can be divided into two categories: direct and indirect approaches [1]. For the latter approach, the controller expression is related to the definition of the identification algorithm, whereas direct methods can identify the parameters of the system model independently of the structure controller applied to the robot. This research study relates to direct methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
