Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter

Yazid, Edwar; Liew, Mohd Shahir; Parman, Setyamartana; Kurian, V.J.

doi:10.1016/j.asoc.2015.05.046

Cited by 18 publications

(2 citation statements)

References 20 publications

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“…Next population is generated by applying the fitness-based selection technique. These steps are repeated until maximum number of generations is reached [12]. This sub-process is then combined into the closed-loop control system with PD controller as shown by Figure 4 to form what the so-called GA-PD controller.…”

Section: B Ga-pd Controllermentioning

confidence: 99%

Rotational Speed Control of Brushless Dc Motor Using Genetic Algorithm Optimized PD Controller

Ardiansyah¹,

Yazid²

2018

indones.j.electron.telecommun.

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Controlling the rotational speed of brushless DC (BLDC) motor is an essential task to improve the transient and the steady state performances under loaded condition. Rotational speed control of BLDC motor using genetic algorithm optimized proportional-derivative (PD) controller to form what the so-called the genetic algorithm-PD (GA-PD) controller is proposed in this paper. Control system is realized in a microcontroller namely a 16MHz Atmega2560 with an absolute encoder as a position sensor. The effectiveness of the GA-PD controller is tested under constant and varying step functions with the Ziegler-Nichols-PD (ZN-PD) controller as a benchmark. Experimental results show that the GA-PD controller has slower speed than the ZN-PD controller, but the latter has overshoot and small oscillations during its steady state condition as a consequent of having a fast rise time.

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Section: B Ga-pd Controllermentioning

confidence: 99%

Rotational Speed Control of Brushless Dc Motor Using Genetic Algorithm Optimized PD Controller

Ardiansyah¹,

Yazid²

2018

indones.j.electron.telecommun.

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“…Identification models must accommodate the non-stationary of the system in terms of timevarying model coefficients. Recent researches in this area can be found in [3][4].…”

Section: Introductionmentioning

confidence: 99%

Application of empirical mode decomposition method for characterization of random vibration signals

Parman

Yazid²

2016

J. Mechatron. Electr. Power Veh. Technol.

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Characterization of finite measured signals is a great of importance in dynamical modeling and system identification. This paper addresses an approach for characterization of measured random vibration signals where the approach rests on a method called empirical mode decomposition (EMD). The applicability of proposed approach is tested in one numerical and experimental data from a structural system, namely spar platform. The results are three main signal components, comprising: noise embedded in the measured signal as the first component, first intrinsic mode function (IMF) called as the wave frequency response (WFR) as the second component and second IMF called as the low frequency response (LFR) as the third component while the residue is the trend. Band-pass filter (BPF) method is taken as benchmark for the results obtained from EMD method.

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Evaluation of Metaheuristics in the Optimization of Laguerre-Volterra Networks for Nonlinear Dynamic System Identification

Costa

Müller

2020

Lecture Notes in Computer Science

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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter

Cited by 18 publications

References 20 publications

Rotational Speed Control of Brushless Dc Motor Using Genetic Algorithm Optimized PD Controller

Rotational Speed Control of Brushless Dc Motor Using Genetic Algorithm Optimized PD Controller

Application of empirical mode decomposition method for characterization of random vibration signals

Evaluation of Metaheuristics in the Optimization of Laguerre-Volterra Networks for Nonlinear Dynamic System Identification

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