A hybrid controller for the speed control of a permanent magnet synchronous motor drive

Elmas, Çetin; Üstün, Oğuz

doi:10.1016/j.conengprac.2007.04.016

Cited by 85 publications

(54 citation statements)

References 17 publications

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“…The five branches use PID control method to guarantee the precision of the movable platform. In order to get good tracking and dynamic performance for the speed loop or the current loop, a PI controller [55,56] is employed for the control of the redundant branch by improving the driving forces. The PID control can be written as Eq.…”

Section: Pid Control Schemementioning

confidence: 99%

Fuzzy Identification and Delay Compensation based on the Force/Position Control Scheme of the 5-DOF Redundantly Actuated Parallel Robot

Wen

Zhang

et al. 2016

Int. J. Fuzzy Syst.

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This paper presents a fuzzy identification method for the dynamic model of the 6PUS-UPU redundantly actuated parallel robot. The T-S fuzzy model is the model of the whole system, the input is the pose of the moving platform and the output is the driving force. The fuzzy model is regarded as the feedback loop between the moving platform and the force branch. The dynamic model is built by Kane's method, and a novel closed-loop force/position hybrid control structure with the pose error for a complex multi-DOF spatial redundantly actuated parallel robot is developed. A proportional-integral force controller is presented based on the structure to obtain an optimal solution under the model identification. The proposed procedure is verified by Matlab/Adams simulation with a 6PUS-UPU simulation platform. The simulation results show that the proposed method is valid for designing the 5-DOF redundantly actuated parallel robot with the movable platform pose error. This paper designs the Smith predictor to solve the delay problem of the redundant force control branch.

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Section: Pid Control Schemementioning

confidence: 99%

Fuzzy Identification and Delay Compensation based on the Force/Position Control Scheme of the 5-DOF Redundantly Actuated Parallel Robot

Wen

Zhang

et al. 2016

Int. J. Fuzzy Syst.

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“…The speed controller is based on a Neural Network Controller and the output of this neural is a reference torque and derivative of this torque T * can be calculated from the speed error (ω) using the same neural controller without integral action. The method is equivalent to generating T * , and T * using the same neural controller and an approximate derivative of the speed error [10]- [13]. Using the two controllers (speed and current) to produce the reference dq voltages.…”

Section: Speed Loopmentioning

confidence: 99%

“…The prime characteristic of an adaptive system is the presence of a so-called reference model as part of the system, which can appear under various forms [2]- [10]. Fig.3, the proposed neural network estimator for stator resistance and torque constant (second method).…”

Section: Neural Network Based Estimator For Torque Constant and Statomentioning

confidence: 99%

Artificial Neural Network Control of Permanent Magnet Synchronous Motor

Saafan¹,

Haikal²,

Saraya³

et al. 2012

IJCA

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This paper presents a neural network controller for permanent magnet synchronous motor (PMSM). The neural controller is used for torque ripple minimization of this type of motors. Two methods of neural controller design are used. The first method is based on two loop controllers (current controller and speed controller). The second method is based on estimation of torque constant and stator resistance in PMSM. The q-axis inductance is modeled off-line according to q-axis stator current. The neural weights are initially chosen small randomly and a model reference control algorithm adjusts those weights to give the optimal values. The neural network parameter estimator has been applied to flux linkage torque ripple minimization of the PMSM. Simulation results using the two methods are compared together. Moreover, the suggested algorithms when compared with other controllers show great success in torque ripples reduction.

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“…In recent years, many scholars have devoted their research to applying sliding mode variable structure control (SMC) to PMSMs because SMC has good robustness to parameter perturbations, external disturbances and inaccurate mathematical description [11][12][13][14][15]. In [11], a variable parameter SMC was studied using reaching law approach.…”

Section: Introductionmentioning

confidence: 99%

“…This method can improve dynamic and static performances, but chattering problem is not suppressed effective. A neuron-fuzzy sliding-mode controller was reported for PMSM control system in [12]. This method weakens the effects of switching control and chattering.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonsingular Terminal Sliding Model Control and Its Application to Permanent Magnet Synchronous Motor Drive System

Liu

Zhou²

2016

MATEC Web Conf.

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Abstract.To improve the dynamic performance of permanent magnet synchronous motor(PMSM) drive system, a adaptive nonsingular terminal sliding model control((NTSMC) strategy was proposed. The proposed control strategy presents an adaptive variable-rated exponential reaching law which the L1 norm of state variables is introduced. Exponential and constant approach speed can adaptively adjust according to the state variables' distance to the equilibrium position.The proposed scheme can shorten the reaching time and weaken system chatting. The method was applied to the PMSM speed servo system, and compared with the traditional terminal-sliding-mode regulator and PI regulator. Simulation results show that the proposed control strategy can improve dynamic, steady performance and robustness.

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A hybrid controller for the speed control of a permanent magnet synchronous motor drive

Cited by 85 publications

References 17 publications

Fuzzy Identification and Delay Compensation based on the Force/Position Control Scheme of the 5-DOF Redundantly Actuated Parallel Robot

Fuzzy Identification and Delay Compensation based on the Force/Position Control Scheme of the 5-DOF Redundantly Actuated Parallel Robot

Artificial Neural Network Control of Permanent Magnet Synchronous Motor

Adaptive Nonsingular Terminal Sliding Model Control and Its Application to Permanent Magnet Synchronous Motor Drive System

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