An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems

Riaz, Saleem; Hui, Lin; Waqas, Muhammad; Afzal, Farkhanda; Wang, Kai; Saeed, Nasir

doi:10.1155/2021/5975158

Cited by 11 publications

(7 citation statements)

References 66 publications

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“…The key to restraining current harmonics, reducing total harmonic distortion (THD) and improving power quality lies in the control of AC current [13]. The literature [14] adopted the current control strategy based on PI control, which is widely used, but the control of AC harmonic by PI controller is not ideal.…”

Section: Introductionmentioning

confidence: 99%

A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement

Wang¹,

Luo²,

Riaz³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Due to attractive features, including high efficiency, low device stress, and ability to boost voltage, a Vienna rectifier is commonly employed as a battery charger in an electric vehicle (EV). However, the 6k ± 1 harmonics in the acside current of the Vienna rectifier deteriorate the THD of the ac current, thus lowering the power factor. Therefore, the current closed-loop for suppressing 6k ± 1 harmonics is essential to meet the desired total harmonic distortion (THD). Fast repetitive control (FRC) is generally adopted; however, the deviation of power grid frequency causes delay link in the six frequency fast repetitive control to become non-integer and the tracking performance to deteriorate. This paper presents the detailed parameter design and calculation of fractional order fast repetitive controller (FOFRC) for the non-integer delay link. The finite polynomial approximates the non-integer delay link through the Lagrange interpolation method. By comparing the frequency characteristics of traditional repetitive control, the effectiveness of the FOFRC strategy is verified. Finally, simulation and experiment validate the steadystate performance and harmonics suppression ability of FOFRC.

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Section: Introductionmentioning

confidence: 99%

A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement

Wang¹,

Luo²,

Riaz³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…However, most scholars focus on nonsystem control problems, and research on system iterative learning control problems is limited [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fast Iterative Learning Control for Linear Discrete Systems with Parametric Disturbance and Measurement Noise

Lei

Riaz

Manzoor³

2022

Mathematical Problems in Engineering

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Precise industrial control technology is constantly in need of accurate and strong control. Error convergence for a typical linear system is very minimal when using a conventional iterative learning control strategy. This study develops a quick iterative learning control law to address this issue. We have presented a new PD iterative learning control approach which is basically grounded on backward error and control parameter rectification for a class of linear discrete time-invariant (LDTI) systems. We have deliberated the repetitive system, which has constraint disturbance and measurement noise. First, we have developed a form of the faster learning law along with a full explanation of the algorithm’s control factor generation process. And then, using the vector method in conjunction with the theory of spectral radius, sufficient conditions for the algorithm’s convergence are introduced for parameter estimation with no noise, parameter uncertainty but excluding the noise, parameter uncertainty with small perturbations, and noise in four different scenarios. Eventually, results show that convergence depends on the control law’s learning factor, the correction term, the factor of association, and the learning interval. Ultimately, the simulation results indicate that suggested approach has a faster error convergence as compared with classical PD algorithm.

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“…Also, it usually results in higher current harmonics and torque ripples with a variable switching pattern. Some research work has also been done in the eld of iterative learning controller [16][17][18]. Several other modi cations and improvements have been suggested in the literature to overcome FOC and DTC drawbacks [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Current Control of Permanent Magnet Synchronous Motors Using Improved Model Predictive Control

Nawaz

Dou

Riaz

et al. 2022

Mathematical Problems in Engineering

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Model predictive control (MPC) is a powerful tool for the control of permanent magnet synchronous motors. However, conventional MPC permits using a single voltage vector during one control interval. This results in higher current distortions and large torque ripples. Sensitivity to control parameters is another issue associated with conventional MPC. The duty cycle suggests using an active vector and a null vector during one sampling interval. The method needs excessive computational and prediction effort. Furthermore, a necessary zero vector as the second vector might not give the optimal results. To overcome the problems of computational burden, this paper proposes that a reference voltage vector can be calculated and used to determine the voltage vector to be used for the next interval. This reduces the computational effort to a minimum. Furthermore, it is proposed that the second vector can either be active or null. To overcome the problem of parameter dependence, an electromotive force is calculated on basis of previous values. Simulations have been carried out to verify the efficacy of the proposed method.

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An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems

Cited by 11 publications

References 66 publications

A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement

A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement

A Novel Fast Iterative Learning Control for Linear Discrete Systems with Parametric Disturbance and Measurement Noise

Current Control of Permanent Magnet Synchronous Motors Using Improved Model Predictive Control

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