Improved Deadbeat Predictive Current Control Combined Sliding Mode Strategy for PMSM Drive System

A fast terminal sliding mode control is proposed in this paper for improving the dynamic performance and robustness of a permanent magnet in-wheel motor system driven by a voltage source inverter. Firstly, a fast terminal sliding mode approaching law was designed to accelerate the approaching rate of the control system. Then, a torque load observer was designed to compensate for disturbances and uncertainties. Finally, fuzzy rules were designed to suppress the chattering phenomenon. Simulation and experimental results demonstrated that the fast terminal sliding mode control strategy presented better response speed than the conventional sliding mode control strategy. It had better dynamic performance and anti-interference and effectively reduced the chattering phenomenon in the control process.

show abstract

“…By combining (2) and (4), the dynamic mathematical model of the PMIWMs can be stated as follows [38]:…”

Section: Ftsmc Algorithm For Controlling the Systemmentioning

confidence: 99%

Fast Terminal Sliding Mode Control of Permanent Magnet In-Wheel Motor Based on a Fuzzy Controller

Huang

Pan

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…A fuzzy sliding-mode speed controller applied to PMSM can be found in [12]. In [15], a hybrid control method based on sliding-mode controller was applied to the closed loop control system of PMSM, and the results are very conclusive regarding the effectiveness of the sliding-mode controller. In addition, a neural network sliding-mode control method was proposed in [16] to improve speed tracking precision.…”

Section: Introductionmentioning

confidence: 99%

A New Reaching Law for Antidisturbance Sliding-Mode Control of PMSM Speed Regulation System

Wang

Feng

Zhang

et al. 2020

IEEE Trans. Power Electron.

272

115

View full text Add to dashboard Cite

In this paper, in order to optimize the dynamic performance of the permanent magnet synchronous motor (PMSM) speed regulation system, a nonlinear speed-control algorithm for the PMSM control systems using sliding-mode control (SMC) is developed. First, a sliding-mode control method based on a new sliding-mode reaching law (NSMRL) is proposed. This NSMRL includes the system state variable and the power term of sliding surface function. In particular, the power term is bounded by the absolute value of the switching function, so that the reaching law can be expressed in two different forms during the reaching process. This method can not only effectively suppresses the inherent chattering, but also increases the velocity of the system state reaching to the sliding-mode surface. Based on this new reaching law, a sliding-mode speed controller (SMSC) of PMSM is designed. Then, considering the large chattering phenomenon caused by high switching gain, an improved anti-disturbance sliding-mode speed controller(ADSMSC) method, called SMSC+ESO method, is developed. This method introduces an extended state observer (ESO) to observe the lumped disturbance and adds a feedforward compensation item based on the observed disturbances to the SMSC. Finally, simulation and experimental results both show the validity of the proposed control method. Index Terms-permanent magnet synchronous motor (PMSM), anti-disturbance sliding mode speed controller (ADSMSC), new sliding-mode reaching law (NSMRL), extended state observer (ESO).

show abstract

“…Various implementation methods of the nonlinear control theory have been used in the PMSM control system to improve the dynamic performances of critical issues such as torque disturbances and motor parameter variations [20][21][22]. A disturbance observer has been designed in [22] based on a nonsingular terminal sliding mode strategy to solve timevarying parameters and torque disturbances of the PMSM drive system.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Adaptive Flux-Weakening Strategy for Permanent-Magnet Synchronous Motor Drives Based on Particle Swarm Algorithm

Ismail

Islam

2019

IEEE Trans. Power Electron.

Self Cite

View full text Add to dashboard Cite

Operating in the high-speed range is necessary for high-performance permanent-magnet synchronous motor (PMSM) drives. However, due to the back electromotive force effect, the PMSM is approaching the voltage limit at field decreasing scope. This paper presents a new flux-weakening scheme along with an improved vector control strategy to alleviate the influence of this problem. Control parameters of the antiwindup proportional and integral (AWPI) controller are optimized off-line in relying on an adaptive velocity particle swarm optimization (AVPSO) algorithm. The AVPSO algorithm considers the summation of AWPI measurement error which is the objective function of the optimization problem without knowing the transfer function of the plant. Hence, the tuned flux-weakening controller with a filter is used to set the flux level without saturating the current controllers. Meanwhile, the other controllers of inner and outer loops award a great dynamic and steady-state performance for the PMSM. In the proposed scheme, the flux-weakening control is not dependent on machine parameters that adapts the flux level automatically and provide a fast transition between the constant torque region and the field-weakening region. Effectiveness and advantages of the proposed scheme are presented in this paper through both simulation and experimental results.

show abstract

Improved Deadbeat Predictive Current Control Combined Sliding Mode Strategy for PMSM Drive System

Cited by 184 publications

References 21 publications

Fast Terminal Sliding Mode Control of Permanent Magnet In-Wheel Motor Based on a Fuzzy Controller

Fast Terminal Sliding Mode Control of Permanent Magnet In-Wheel Motor Based on a Fuzzy Controller

A New Reaching Law for Antidisturbance Sliding-Mode Control of PMSM Speed Regulation System

Parameter Optimization of Adaptive Flux-Weakening Strategy for Permanent-Magnet Synchronous Motor Drives Based on Particle Swarm Algorithm

Contact Info

Product

Resources

About