Adaptive Control in Conditions of Constantly Acting Perturbations: an Identification Approach

1998 ◽  
Vol 30 (6) ◽  
pp. 53-63
Author(s):  
Leonid S. Zhitetskiy
Keyword(s):  
Adaptive Control ◽  
Identification Approach

A new multimodel approach by Laguerre filters on sliding window for nonlinear system identification and control

2020 ◽  
Vol 42 (12) ◽  
pp. 2234-2253
Author(s):  
Sameh Adaily ◽  
Abdelkader Mbarek ◽  
Tarek Garna
Keyword(s):  
Adaptive Control ◽  
Predictive Control ◽  
Sliding Window ◽  
Linear Constraints ◽  
Continuous Stirred Tank Reactor ◽  
Parameter Uncertainties ◽  
Identification Procedure ◽  
Online Identification ◽  
Identification Approach ◽  
Adaptive Predictive Control

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.

scholarly journals Multiscale Chebyshev Neural Network Identification and Adaptive Control for Backlash-Like Hysteresis System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuehui Gao ◽  
Ruiguo Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Tracking Error ◽  
Closed Loop System ◽  
Chebyshev Neural Network ◽  
Hysteresis Nonlinearity ◽  
Control Scheme ◽  
Network Identification ◽  
Identification Approach ◽  
Adaptive Control Strategy

An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is proposed for the backlash-like hysteresis nonlinearity system in this paper. Firstly, a MSCNN is introduced to approximate the backlash-like nonlinearity of the system, and then, the Lyapunov theorem assures the identification approach is effective. Afterward, to simplify the control design, tracking error is transformed into a scalar error with Laplace transformation. Therefore, an adaptive control strategy based on the transformed scalar error is proposed, and all the signals of the closed-loop system are uniformly ultimately bounded (UUB). Finally, simulation results have demonstrated the performance of the proposed control scheme.

Comments on 'An adaptive control scheme for robot manipulators'

1989 ◽  
Vol 49 (5) ◽  
pp. 1805-1809
Author(s):  
Yong-Duan Song ◽  
Wei-Bing Gao
Keyword(s):  
Adaptive Control ◽  
Robot Manipulators ◽  
Control Scheme ◽  
Adaptive Control Scheme
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