Recursive least squares based sliding mode approach for position control of DC motors with self-tuning rule

2020 ◽  
Vol 34 (12) ◽  
pp. 5223-5237
Author(s):  
Kwangseok Oh ◽  
Jaho Seo
Keyword(s):  
Least Squares ◽  
Position Control ◽  
Sliding Mode ◽  
Recursive Least Squares ◽  
Dc Motors ◽  
Self Tuning

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

1998 ◽  
Vol 212 (6) ◽  
pp. 437-448 ◽  
Author(s):  
K Warwick ◽  
Y-H Kang
Keyword(s):  
Least Squares ◽  
Adaptive Controller ◽  
Recursive Least Squares ◽  
Industrial Processes ◽  
Industrial Plant ◽  
Parameter Estimator ◽  
Proportional Integral ◽  
Control Scheme ◽  
Self Tuning ◽  
Real Plant

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

scholarly journals A Simulation and Experimental Study on Identification of an Electromechanical System

2022 ◽  
Vol 16 ◽  
pp. 26-33
Author(s):  
Tolgay Kara ◽  
Sawsan Abokoos
Keyword(s):  
Experimental Data ◽  
Nonlinear System ◽  
Least Squares ◽  
Position Control ◽  
Nonlinear Models ◽  
Dead Zone ◽  
Nonlinear Effects ◽  
Recursive Least Squares ◽  
Suitable Model ◽  
Linear And Nonlinear

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

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