Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

Jimenez-Gonzalez, J.; Gonzalez-Montanez, F.; Jimenez-Mondragon, V. M.; Licéaga-Castro, J.; Escarela‐Perez, R.; Olivares-Galván, J. C.

doi:10.3390/act10070143

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…Although GWO generated the best parametric estimation in this research, the Steiglitz-McBride algorithm could work as another alternative when the application requires low computational cost and a low standard deviation in the estimation of each parameter; see Table 8. For example, in [49], it has been shown that the Steiglitz-McBride algorithm is useful in the parametric estimation of electrical machines such as DC motors, brush DC, and brushless AC and gear machines. Also, it is frequently used as an optimizer.…”

Section: Discussionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

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Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

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Section: Discussionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

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“…where λ(k) is a pondered constant value for all k domain. This value is defined as the forgetting factor of the algorithm [25]. The term λ must be included inside the interval (0; 1].…”

Section: Recursive Least-square Identification Algorithmmentioning

confidence: 99%

Modeling and Control of a DC-DC Buck–Boost Converter with Non-Linear Power Inductor Operating in Saturation Region Considering Electrical Losses

Molina-Santana,

Gonzalez-Montañez,

Liceaga-Castro

et al. 2023

Mathematics

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The present work proposes a nonlinear model of a buck–boost DC-DC power converter considering the nonlinear magnetic characteristics of the power inductor and electrical losses of the system. The Euler–Lagrange formalism is used for formulating the proposed model. Previous research works have reported mathematical models to describe power inductor dynamics. However, a gap in the literature remains regarding modeling this kind of element when it operates within power converters. Also, a linear-based controller scheme is proposed to regulate a non-ideal buck–boost DC-DC power converter. A methodology for tuning the proposed controller is presented, which considers the nonlinear model structure of the power converter, the linearization procedure based on an identification process, and a frequency domain analysis based on the approximated linear model. Finally, the tuned control scheme is tested on the nonlinear model of the power converter under several operational conditions showing excellent performance by effectively regulating the output voltage. The results are compared with those derived from alternative control strategies, and a better performance is generally obtained.

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“…Moreover, appropriate modeling for the system is carried out using algorithms such as the linear identified model, nonlinear grey-box model, nonlinear ARX model, and Hammerstein-Wiener model. Hybrid adaptive position controller design in which RLS estimator, disturbance observer and Lyapunov methods are used together [7], RLS estimator method based on a discrete-time sliding mode controller used in the control of the DC motor speed [8], and the RLS approach, which estimates the parameters of the DC motor model [9], can be given as examples to adaptive controller designs. Adaptive designs developed are also tested on other systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive State Feedback Control Method Based on Recursive Least Squares

Levent

Aydoğdu

2022

ELEKTRON ELEKTROTECH

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This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical model of the time-varying system using the input/output data packets of the controller system. Thanks to this block, the system is constantly monitored to update the parameters of the system, which change over time. Linear quadratic regulator (LQR) is renewed according to these updated parameters, and self-adjustment of the system is provided according to the changed system parameters. The Matlab/Simulink state-space model of the variable loaded servo (VLS) system module was obtained for the simulation experiments in this study; then the system was controlled. Moreover, experiments were carried out on the servo control experimental equipment of the virtual simulation laboratories (VSIMLABS). The effectiveness of the proposed new method was observed taking the performance indexes as a reference to obtain the results of the practical application of the proposed method. Regarding the analysis of simulation and experimental results, the proposed approach minimizes the load effect and noise and the system works at high efficiency.

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Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

Cited by 15 publications

References 23 publications

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Modeling and Control of a DC-DC Buck–Boost Converter with Non-Linear Power Inductor Operating in Saturation Region Considering Electrical Losses

Adaptive State Feedback Control Method Based on Recursive Least Squares

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