Intelligent speed control of interior permanent magnet motor drives using a single untrained artificial neuron

Butt, Casey; Rahman, M.A.

doi:10.1109/iemdc.2011.5994628

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The main factors causing thrust fluctuations include cogging and end effects caused by core slots, the viscous friction caused by the motion of the mover, and sliding friction disturbances. [15][16][17] Vector control is an effective control method for linear motors, usually including the following control methods: rotor flux orientation control, stator flux linkage control, maximum thrust current ratio control, field weakening control, i d = 0 vector control, direct torque control, constant magnetic control, and so on. Among them, i d = 0 vector control is known as a control method with good control precision and rapidity.…”

Section: Pmlsm Mathematical Modelmentioning

confidence: 99%

Improved neuroendocrine intelligent control for a permanent magnet linear synchronous motor

Shu

Liu

Xing

et al. 2019

Measurement and Control

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Given the complex characteristics of permanent magnet linear synchronous motors and the external interference encountered during their operation, controlled precision and efficiency are necessary. In this paper, the mechanism of biological endocrine hormone regulation is analyzed, and an intelligent controller based on the obtained neuroendocrine algorithm is implemented in the control system of a permanent magnet linear synchronous motor. The controller mainly includes a hypothalamic regulation module, a single-neuron proportion integration differential module, and an ultrashort feedback module. It is designed and referenced to the long feedback, short feedback, and ultrashort feedback loop mechanisms of neuroendocrine hormone regulation and abides by the principle of human neuroendocrine hormone regulation. The antagonistic hormone regulation module achieves rapid and stable elimination of errors through the fusion of enhanced regulation with regulation inhibition, and the single-neuron proportion integration differential module enhances the adaptive and self-learning capabilities of the control system. The proposed control is successfully used in a permanent magnet linear synchronous motor, and the experimental results show that the controller presents many advantages, such as fast dynamic responses, strong online adjustment ability, and good running stability in the control system, all of which improve the robustness of the control system.

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Section: Pmlsm Mathematical Modelmentioning

confidence: 99%

Improved neuroendocrine intelligent control for a permanent magnet linear synchronous motor

Shu

Liu

Xing

et al. 2019

Measurement and Control

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show abstract

“…Moreover, these controllers are inherently unable to simultaneously meet good step reference tracking and good load torque rejection. Many other different control techniques of varying degrees of complexity have appeared based on the nature of drive applications, such as non-linear control with adaptive backstepping technique [4][5][6], variable structure controller (VSC) with two degrees of freedom control [7], sliding mode controller (SMC) [8,9], VSC [10,11], integral VSC combined with linear quadratic regulator [12], fuzzy logic controller (FLC) [13][14][15][16], adaptive FLC [17,18], neural network controller (NNC) [19], neuro-fuzzy controller (NFC) [20], and genetic-based FLC [21].…”

Section: Introductionmentioning

confidence: 99%

Robust Sliding Mode Speed Controller-based Model Reference Adaptive System (MRAS) and Load Torque Estimator for Interior Permanent Magnet Synchronous Motor (IPMSM) Drives

Zaky

2015

Electric Power Components and Systems

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“…However, the PMSM system is a complex nonlinear system with multiple coupled states and unavoidable and unmeasured disturbances, as well as parameter perturbations. To achieve high-performance control, various advanced control methods have been proposed, such as adaptive control [3], robust control [4], sliding mode control [5,6], optimal control [7], backstepping control [8], predictive control [9], fuzzy control [10], neural network control [11], finite-time control [12], fractional order control [13,14], and intelligent control [15]. These methods have increased the dynamic and steady state performance of PMSM systems to some degree.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Disturbance Observer-Based Fractional Second-Order Nonsingular Terminal Sliding Mode Control for PMSM Position Regulation System

Jiang

Kang

2015

Mathematical Problems in Engineering

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This paper investigates the position regulation problem of permanent magnet synchronous motor (PMSM) subject to parameter uncertainties and external disturbances. A novel fractional second-order nonsingular terminal sliding mode control (F2NTSMC) is proposed and the finite time stability of the closed-loop system is ensured. A sliding mode disturbance observer (SMDO) is developed to estimate and make feedforward compensation for the lumped disturbances of the PMSM system. Moreover, the finite-time convergence of estimation errors can be guaranteed. The control scheme combining F2NTSMC and SMDO can not only improve performance of the closed-loop system and attenuate disturbances, but also reduce chattering effectively. Simulation results show that the proposed control method can obtain satisfactory position tracking performance and strong robustness.

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Intelligent speed control of interior permanent magnet motor drives using a single untrained artificial neuron

Cited by 3 publications

References 17 publications

Improved neuroendocrine intelligent control for a permanent magnet linear synchronous motor

Improved neuroendocrine intelligent control for a permanent magnet linear synchronous motor

Robust Sliding Mode Speed Controller-based Model Reference Adaptive System (MRAS) and Load Torque Estimator for Interior Permanent Magnet Synchronous Motor (IPMSM) Drives

Sliding Mode Disturbance Observer-Based Fractional Second-Order Nonsingular Terminal Sliding Mode Control for PMSM Position Regulation System

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