Robust Flatness Control with Extended Luenberger Observer for PMSM Drive

Sriprang, Songklod; Nahid‐Mobarakeh, Babak; Pierfederici, Serge; Takorabet, Noureddine; Bizon, Nicu; Mungporn, Pongsiri; Thounthong, Phatiphat

doi:10.1109/itec-ap.2018.8432600

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…8. Online Network Parameters Learnings: The online parameters learning are achieved by online tuning of the connective weights w 6 o in the output layer, the connective weights w 4 l in the rule and recurrent layer, the connective weights w 3 MP in the Legendre layer, and the mean m ij and standard deviations σ ij of the membership functions in the membership layer.…”

Section: Rlfnn Output Layermentioning

confidence: 99%

“…Owing to rare-earth permanent magnets (PMs) being mounted on the rotor, the Interior permanent magnet synchronous motor (IPMSM) have a high torque and power density, wide speed operating range, and high efficiency. Therefore, they are widely used in electric vehicles (EVs) and vehicular applications [1], [2], [3], [4], such as starters/alternators, traction motor, power steering, and…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

et al. 2023

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The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN's online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.INDEX TERMS Permanent-magnet assisted synchronous reluctance motor (PMASynRM), computed torque control (CTC), intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN), maximum torque per ampere (MTPA).

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Section: Rlfnn Output Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

et al. 2023

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“…The load at the DC bus is a 3-phase inverter driving a three AC motor [induction motor or permanent magnet synchronous motor (PMSM)], as a vehicle traction drive. So far, algorithms based on differential flatness have been successfully applied to power converters (e.g., 3-phase inverter and rectifier, interleaved boost converter, modular multilevel converter) [17,18,20,21], permanent magnet synchronous motor and AC servomotor [19,22,23]. Based on these previous works, the purpose of this article is to extend the use of differential flatness algorithm in an embedded DC microgrid (i.e., EV powertrain) to manage optimally its operation during static and dynamic operations.…”

Section: Power Converter Structurementioning

confidence: 99%

“…This approach has enabled the system to be an alternative representative, of which motion planning and regulator tuning is clear-cut. This theory has lately been utilized in a variety of networks in different scientific domains [17][18][19][20][21][22][23]. Compared to the nonlinear algorithm (i.e., sliding mode, Lyapunov, fuzzy logic) reported in [13][14][15], nonlinear algorithms based on differential flatness require the use of trajectory planning to implement the control laws.…”

Section: Introductionmentioning

confidence: 99%

Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

et al. 2020

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This article proposes a new control law for an embedded DC distributed network supplied by a supercapacitor module (as a supplementary source) and a battery module (as the main generator) for transportation applications. A novel control algorithm based on the nonlinear differential flatness approach is studied and implemented in the laboratory. Using the differential flatness theory, straightforward solutions to nonlinear system stability problems and energy management have been developed. To evaluate the performance of the studied control technique, a hardware power electronics system is designed and implemented with a fully digital calculation (real-time system) realized with a MicroLabBox dSPACE platform (dual-core processor and FPGA). Obtained test bench results with a small scale prototype platform (a supercapacitor module of 160 V, 6 F and a battery module of 120 V, 40 Ah) corroborate the excellent control structure during drive cycles: steady-state and dynamics.

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“…However, these methods are not applicable for non-linear estimation systems. In order to overcome this drawback, several observers were presented in the previously reported works [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Position and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization

Allaoui¹,

Laamari²,

Chafaa³

et al. 2021

JESA

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In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBO-EKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSO-EKF approaches either in resolution or in convergence speed.

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Robust Flatness Control with Extended Luenberger Observer for PMSM Drive

Cited by 14 publications

References 15 publications

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

Position and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization

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