scholarly journals Online identification of a robot using batch adaptive control

2003 ◽  
Vol 36 (16) ◽  
pp. 921-926
Author(s):  
Björn Bukkems ◽  
Dragan Kostić ◽  
Bram de Jager ◽  
Maarten Steinbuch
2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.


2020 ◽  
Vol 42 (12) ◽  
pp. 2234-2253
Author(s):  
Sameh Adaily ◽  
Abdelkader Mbarek ◽  
Tarek Garna

This paper proposes an online identification procedure on sliding window with the synthesis of a nonlinear adaptive predictive control for a new nonlinear model resulting in multimodel approach. Such a model is entitled ARX-Laguerre multimodel obtained by expanding the conventional ARX multimodel on independent Laguerre orthonormal bases. It allows a significant parameter number reduction as well as a simple recursive representation compared with the ARX-multimodel. This parametric reduction is provided from an optimal iteratif identification approach of the Laguerre poles presented in Adaily et al. (2013). We propose to combine and carry out this identification approach on a sliding window to achieve an online identification procedure of the ARX-Laguerre multimodel for real time procedure depending on Fourier coefficients that are identified by a regularized square error. This property allows to synthese a new nonlinear adaptive predictive control on sliding window. We develop the general form of a new predictor and so, we propose an optimization algorithm formulated as a quadratic programming (QP) under linear constraints for an adaptive control law. The performances of the proposed online identification procedure and the developed nonlinear adaptive control algorithm are illustrated on a benchmark system as the continuous stirred tank reactor system (CSTR) with respect to the process parameter uncertainties.


2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Yuling Li ◽  
Yixin Yin ◽  
Sen Zhang

It is well known that parameter convergence in adaptive control can bring about an improvement of system performance, including accurate online identification, exponential tracking, and robust adaptation without parameter drift. However, strong persistent-excitation (PE) or sufficient-excitement (SE) conditions should be satisfied to guarantee parameter convergence in the classical adaptive control. This paper proposes a novel adaptive control to guarantee parameter convergence without PE and SE conditions for nonlinear teleoperation systems with dynamic uncertainties and time-varying communication delays. The stability criterion of the closed-loop teleoperation system is given in terms of linear matrix inequalities. The effectiveness of this approach is illustrated by simulation studies, where both master and slave are assumed to be two-link manipulators with full nonlinear system dynamics.


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