Generalized super-twisting sliding mode control of permanent magnet synchronous motor based on sinusoidal saturation function

Yang, Hao; Yan, Binbin; Chien, Ying-Ren

doi:10.1587/elex.19.20220066

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…To optimize the performance of the LIM predictive control system, the adjusting of design parameters and gains of the observer is necessitated. Therefore, this study employs a particle swarm optimization algorithm to determine the optimal values of these parameters, namely 13 , , , g g m n .The range of values for each parameter must adhere to the requirements outlined in the Lyapunov stability analysis in the previous section [24,25,26].…”

Section: Adaptive Observer Parameter Self-tuningmentioning

confidence: 99%

“…Sliding mode flux observer (SMO) based on the principle of sliding mode variable structure control is unaffected by external disturbances and has strong robustness to parameter variations in the system. Consequently, many experts and scholars have conducted in-depth research on this topic [11,12,13,14,15]. Reference [16] proposed a method based on error backpropagation neural network for optimizing the parameters of rotor position observer.…”

Section: Introductionmentioning

confidence: 99%

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Predictive current control method based on IPSO-ASMO for LIM

2024

IEICE Electron. Express

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This paper presents a new method to overcome the degraded control performance of linear induction motors (LIM) due to end effects. The method employs a parameter-self-tuning adaptive sliding mode observer (ASMO) and model predictive current control (MPCC), which first develops a LIM mathematical model considering end effects and then designs the MPCC to strengthen the control. To improve the accuracy of the flux observation, the method develops the ASMO and analyzes the stability of the observer. For parameter tuning, the method utilizes an improved particle swarm optimization algorithm (IPSO). Overall, the experimental findings validate the effectiveness of the proposed algorithm.

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Section: Adaptive Observer Parameter Self-tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive current control method based on IPSO-ASMO for LIM

2024

IEICE Electron. Express

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“…This can cause high-frequency ji er with rapid and frequent switching on the sliding mode surface, and reduce the control accuracy of the system. A study [29] added a sinusoidal saturation function, sat (s), in the super-twisting control law to reduce the system ji er. However, the saturation function presents the problem of selecting the appropriate thickness of the boundary layer, which is extremely large to lead to the insufficient control accuracy of the system, and excessively small to cause oscillations.…”

Section: Construction Of Adaptive Quasi-super-twisting Sliding Mode C...mentioning

confidence: 99%

Adaptive Quasi-Super-Twisting Sliding Mode Control for Flexible Multistate Switch

Ma,

Wang,

Wang

et al. 2024

Energies

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The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system.

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“…The PI controller is usually adopted in the speed and current regulation of PMSMs [4]; however, it cannot work well in a large operating region because the controller parameters are generally tuned based on an identified operating condition. With the improvement of processor, intelligent and nonlinear control methods have been gradually introduced, such as fuzzy control [5], neural network control [6], sliding mode control [7] and model predictive control (MPC) [8], have been gradually introduced. The theory of PCH [9] and the corresponding interconnection and damping assignment passivity-based control (IDA-PBC) [10] have been widely used in industrial applications and process control [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Current-constraint speed regulation for PMSM based on port-controlled Hamiltonian realization and deep deterministic policy gradient

Wang,

Liu,

Wang

et al. 2024

IEICE Electron. Express

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To ensure overcurrent protection, fast dynamic performance and good robustness, a novel speed regulation controller is proposed. The interconnection and damping assignment passivity-based control (IDA-PBC) of Port-controlled Hamiltonian (PCH) systems has the advantages of simple structure and explicit physical meaning. On account of fast dynamic performance and 𝑞-axis current-constraint are contradictory, this paper presents a current-constraint speed regulation method for the permanent magnet synchronous motor (PMSM) based on port-controlled Hamiltonian (PCH) realization and deep deterministic policy gradient (DDPG) to balance them. First, a modified IDA-PBC controller with integral action(IA) is constructed, which can regulate the speed and the current of the PMSM simultaneously, and be more suitable for practical applications due to the addition of IA. For both current-constraint and fast dynamic performance, the reinforcement learning of DDPG is utilized to find the optimal parameters of the controller. Finally, experiments are carried out to verify the effectiveness and robustness of the method.

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Generalized super-twisting sliding mode control of permanent magnet synchronous motor based on sinusoidal saturation function

Cited by 13 publications

References 24 publications

Predictive current control method based on IPSO-ASMO for LIM

Predictive current control method based on IPSO-ASMO for LIM

Adaptive Quasi-Super-Twisting Sliding Mode Control for Flexible Multistate Switch

Current-constraint speed regulation for PMSM based on port-controlled Hamiltonian realization and deep deterministic policy gradient

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