A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint

Szabat, K.; Wróbel, Karol; Dróżdż, K.; Janiszewski, Dariusz; Pajchrowski, Tomasz; Wójcik, Adrian

doi:10.3390/en13082056

Cited by 23 publications

(24 citation statements)

References 52 publications

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“…The Laplace CTTF model may be transformed into a continuous frequency response function (CFRF) by applying s = jω, where s is the Laplace operator and ω is the angular frequency (ω = 2πf, f -continuous frequency in Hz). In the result, (1) is transformed to (2). Sampled data (measurements) are delivered in the time domain and need to be transformed to the frequency domain by application of discrete Fourier transform (DFT).…”

Section: Frequency Model Of Electric Drive With Multi-resonant Mechanical Partmentioning

confidence: 99%

“…The results of the best fit at the same starting parameters for each representation are presented in Figure 4. For an evaluation of method, L R was set to two blocks in (2). A total of nine parameters were searched, with one parameter representing the total moment of inertia and each of the resonant blocks requiring four parameters (3).…”

Section: Identification From Synthetic Datamentioning

confidence: 99%

“…This assists in understanding electromechanical system dynamic behavior in many practical cases. It also provides a tool to the simulate behavior of the electromechanical system in different conditions [1,2]. Deep knowledge of the model will be helpful in selecting a classical control method and tuning it and using a predictive control algorithm [3,4] or one of the adaptive control methods [2,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…It also provides a tool to the simulate behavior of the electromechanical system in different conditions [1,2]. Deep knowledge of the model will be helpful in selecting a classical control method and tuning it and using a predictive control algorithm [3,4] or one of the adaptive control methods [2,5,6]. Another wide range of applications that are based on model knowledge are fault diagnosis methods, where the model is used to calculate residuum between the model and the real system.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part

Łuczak

2021

Energies

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Knowledge of a direct-drive model with a complex mechanical part is important in the synthesis of control algorithms and in the predictive maintenance of digital twins. The identification of two-mass drive systems with one low mechanical resonance frequency is often described in the literature. This paper presents an identification workflow of a multi-resonant mechanical part in direct drive with up to three high-frequency mechanical resonances. In many methods, the identification of a discrete time (DT) model is applied, and its results are transformed into a continuous-time (CT) representation. The transformation from a DT model to a CT model has limitations due to nonlinear mapping of discrete to continuous frequencies. This problem may be overcome by identification of CT models in the frequency domain. This requires usage of a discrete Fourier transform to obtain frequency response data as complex numbers. The main work presented in this paper is the appropriate fitting of a CT model of a direct-drive mechanical part to complex number datasets. Fitting to frequency response data is problematic due to the attraction of unexcited high frequency ranges, which lead to wrong identification results of multi-mass (high order) drive systems. Firstly, a CT fitting problem is a nonlinear optimization problem, and, secondly, complex numbers may be presented in several representations, which leads to changes in the formulation of the optimization problem. In this paper, several complex number representations are discussed, and their influence on the optimization process by simulation evaluation is presented. One of the best representations is then evaluated using a laboratory setup of direct drive with unknown parameters of three high mechanical resonance frequencies. The mechanical part of the examined direct drive is described by three mechanical resonances and antiresonances, which are characteristic of a four-mass drive system. The main finding is the addition of frequency boundaries in the identification procedure, which are the same as those in the frequency range of the excitation signal. Neither a linear least-square algorithm nor a nonlinear least-square algorithm is suitable for this approach. The usage of nonlinear least-square algorithm with constraints as a fitting algorithm allows one to solve the issue of modeling multi-mass direct-drive systems in the frequency domain. The second finding of this paper is a comparison of different cost functions evaluated to choose the best complex number representation for the identification of multi-mass direct-drive systems.

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Section: Frequency Model Of Electric Drive With Multi-resonant Mechanical Partmentioning

confidence: 99%

Section: Identification From Synthetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part

Łuczak

2021

Energies

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“…The examined PMSM can be modeled using ordinary simplified assumptions: the rotor has no windings; the flux produced by the rotor is constant; and eddy currents, saturation, and temperature effects can be ignored. These assumptions are relevant to the sensorless approach method [10,[27][28][29][30]. The model of the PMSM using dq-frame rotating with the rotor can be described as follows:…”

Section: Model Of a Pmsmmentioning

confidence: 99%

Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

Urbański

Janiszewski

2021

Energies

Self Cite

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This paper presents a method for shaft position estimation of a synchronous motor with permanent magnets. Zero speed and very low speed range are considered. The method uses the analysis of high-frequency currents induced by the introduction of additional voltage in the control path in the stationary coordinate system associated with the stator. An artificial neural network estimates the sine and cosine values necessary in the Park’s transformation units. This method can achieve satisfactory accuracy in the case of low asymmetry of inductance in the direct and quadrature axes of the coordinate system associated with the rotor. The TensorFlow/Keras package was used for artificial network calculations and the scikit-learn package for preprocessing. Aggregating the outputs of several artificial neural networks provides an opportunity to reduce the resultant estimation error. The use of as few as four networks has enabled the error to be reduced by approximately 20% compared to a single example network.

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Constrained control of flexible-joint lever arm based on uncertainty estimation with data fusion for correcting measurement errors

Samiei,

Mirzaei,

Rafatnia

2024

Nonlinear Dyn

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A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint

Cited by 23 publications

References 52 publications

Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part

Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part

Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

Constrained control of flexible-joint lever arm based on uncertainty estimation with data fusion for correcting measurement errors

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