A Novel Predictive Direct Torque Controller for Induction Motor Drives

Ouhrouche, M.; Errouissi, Rachid; Trzynadlowski, A.M.; Tehrani, Kambiz; Benzaioua, Ammar

doi:10.1109/tie.2016.2558140

Cited by 49 publications

(36 citation statements)

References 35 publications

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“…However, if ρ is set too large, it will result in shaking during steady-state. This can be explained by (15); if ρ is very large, Q will change dramatically, it will fail to obtain the optimal values of Q. e threshold is less important, it can be regarded as an empirical value. It's appropriate to set it within 1 × 10 −4 −1 × 10 −7 .…”

Section: Sensitivity Analysis Of the Proposed Algorithmmentioning

confidence: 99%

“…The inertia identification methods in [10][11][12] estimate offline and a specific speed command, such as a sine wave, is implemented in such identification methods. For load torque estimation, an observer is mostly widely used [13][14][15]. One assumption in conventional observer design is that the moment of inertia in the system model is known.…”

Section: Introductionmentioning

confidence: 99%

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An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

et al. 2018

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Abstract:In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.

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Section: Sensitivity Analysis Of the Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

et al. 2018

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“…Therefore, compared with the field‐oriented control method, DTC has a simpler structure, faster dynamics response, and lower parameter sensitivity . However, the DTC also presents some drawbacks such as high and variable switching frequency, large torque and flux ripples, and poor performance at low speeds . The torque ripple and variable switching frequency are mainly caused by the use of hysteresis comparators .…”

Section: Introductionmentioning

confidence: 99%

A novel model‐predictive direct control for induction motor drives

Yang

Liu

et al. 2019

IEEJ Transactions Elec Engng

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Three-phase induction motor (IM) drives play an important role in the variable speed applications from fractional horsepower to multi Megawatt power; it controls and evaluates them to be an interesting subject. This study presents a novel model-predictive direct torque control (MPDTC) for IM drives. It combines the direct torque control (DTC) and finite control set model-predictive control (FCS-MPC) methods, and uses the predictive look-up tables to enhance the overall performance of IM as the good steady-state performance, lower switching frequency, and to achieve relatively low computational complexity. The simulations and experiments are performed to verify the effectiveness of the proposed control strategy. As the main conclusion of this work, it overcomes the disadvantages of the DTC method such as the high torque and stator flux ripples and those of the FCS-MPC method such as the large amount of calculation and high average switching frequency.

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“…7 Especially in the last years, a large number of applications of predictive controllers have been studied to improve the induction motor performance. 8,9 One of the main limiting factors of using predictive controllers is the computational cost involved, especially for systems with fast dynamics, as with 3-phase induction motor. This article presents a predictive controller for activating induction motor 10 with the addition of an external control loop for eliminating steady-state error.…”

Section: Introductionmentioning

confidence: 99%

A predictive control framework for 3‐phase induction motors modeled in natural variables

Cavalca

Nied

Cavalca

et al. 2017

Optim Control Appl Methods

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Summary This paper presents a nonlinear control approach for 3‐phase induction motors. The proposed structure combines a 3‐phase predictive controller with an integrative reference filter. The predictive controller is designed based on an induction motor model established in natural variables (without using transformations), which is a nonlinear and time‐variant one. This model enables the controller to work independently with the supply voltages, considering unbalanced situations. A dynamic evaluation of the state equation coefficients is used to perform the process variables prediction, thereby executing a point‐to‐point linearization. The conversion of the rotation speed and stator flux modulus reference values is realized by a integrative 3‐phase referrer, which acts as a reference filter, expressing the references as 3‐phase signals and acting as an integrator to eliminate steady‐state errors. Also, a constraint feature is implemented, to reduce the currents. Simulation results satisfactorily show the proposed control architecture characteristics for various reference values and for motor operation as a brake and with load variation.

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A Novel Predictive Direct Torque Controller for Induction Motor Drives

Cited by 49 publications

References 35 publications

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

A novel model‐predictive direct control for induction motor drives

A predictive control framework for 3‐phase induction motors modeled in natural variables

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