Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

Khiabani, Ataollah Gogani; Heydari, Ali

doi:10.1049/iet-pel.2019.1339

Cited by 10 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Permanent magnet synchronous motor is developed from the traditional electrically excited synchronous motor, which eliminates the structures such as excitation winding and commutation transposition, introduces the permanent magnet structure, reduces the reactive power exchange between the motor and the power grid, and makes the working performance of the motor more reliable [11,12]. Permanent magnet synchronous motor is mainly divided into built-in type and surface mounted type.…”

Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 99%

See 1 more Smart Citation

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

View full text Add to dashboard Cite

In recent years, the application range of electric energy in modern industry has gradually expanded. Permanent magnet synchronous motor has the characteristics of high efficiency and energy saving and has obvious advantages in traction application. In order to achieve good vector control of permanent magnet synchronous motor in the full speed range, Research on model reference adaptive system (MRAS) and pulsating high frequency injection method to construct the permanent magnet synchronous motor vector control system and combined with the flux weakening control algorithm to control the motor under the condition of limited inverter output, so as to realize the sensorless vector control of the motor in the full speed range. The difference between the estimated value of the algorithm and the actual value is compared on the actual experimental platform to verify the feasibility of the control algorithm. The experimental results show that, under medium and high-speed working conditions, the motor speed and rotor position tracking accuracy of MRAS algorithm is high, and the tracking error is less than 0.01rad. The pulsating high-frequency injection method can accurately track the permanent magnet synchronous motor under lowspeed working conditions, the speed error is less than 1n / R / min, and the rotor position error is less than 0.03rad. The static and dynamic performance of the control system is good, It can better deal with the sudden change of motor load. Using MRAS algorithm and pulsating high frequency injection method to control permanent magnet synchronous motor in full speed range is of great significance to improve the speed control performance of permanent magnet synchronous motor.

show abstract

Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 99%

“…In equation (11), 𝐴𝐴 represents the system state component, 𝑢𝑢 represents the system input, and 𝐵𝐵 and 𝐴𝐴 are matrix parameters. The stator voltage equation of permanent magnet synchronous motor is expressed as state matrix:…”

Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 99%

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

View full text Add to dashboard Cite

show abstract

“…In [6], a modified model predictive control method with the virtual model was proposed to reduce the dynamic tracking error of the position loop and the dynamic tracking error and stability time by generating a virtual reference. Adaptive control methods were adopted in [8][9][10][11][12]. In [8], a feedforward control scheme based on an adaptive observer was proposed for tracking problems with unknown mechanical parameters and unknown load torque.…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive law of inertia was deduced and Popov's theory was introduced into the stability analysis of the adaptive observer to improve the dynamic response performance of the controller. In [9], a control method for the PMSM based on adaptive dynamic programming was proposed to establish two neural networks to control torque and realise low torque ripple under an unknown load. In [10], an adaptive optimal control method was proposed for the dynamic disturbance of velocity and current.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Load Simulation Algorithm Based on an Inertia Simulation Predictive Model

Lin

Wang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

In this study, an electric dynamic load simulation system (EDLSS) algorithm was proposed based on an inertia simulation predictive model to mitigate strong-coupling torque disturbance and load torque fluctuation caused by the change in the motion state of a bearing system. First, the inertia simulation model was proposed by combining the dynamic equations of both the EDLSS and the target system. The aforementioned inertia simulation model converted the conventional realisation method of the inertia simulation into the tracking of the motion characteristics for the target system under the same working conditions. Next, based on the aforementioned inertia simulation model while considering the strong-coupling torque effect and motor braking state disturbance as two influential factors, an inertia simulation predictive model and a load simulation algorithm were proposed. The predicted speed calculated by the predictive model was consistent with the dynamic characteristics of the target system under the same working conditions and input into the control loop. Based on the analysis of the braking state and power model of the permanent magnet synchronous motor, an energy feedback control method was proposed to improve EDLSS stability caused by the braking state of the loading motor. Finally, the experimental data revealed that the maximum speed fluctuation range of the loading motor was approximately 7.5, which was 84% lower than the range before the application of the aforementioned algorithm, which was about 46.8. Furthermore, the maximum range of the torque ripple was close to 1.5, which was 75% lower than before, which was roughly 6. All experimental data were consistent with simulation data.

show abstract

“…ADP also possesses great practical values. For example, it can be used to obtain the optimal controllers for permanent magnet synchronous motors [18], antilock brake systems [19], dc-dc power converters [20], etc.…”

mentioning

confidence: 99%

Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP

Liang¹,

Liu

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

This article presents a novel neural-network-based optimal event-triggered impulsive control method. First, a novel general-event-based impulsive transition matrix (GITM) is constructed to represent the probability distribution evolving characteristics regarding all system states across the impulsive actions, rather than the prefixed timing sequence. On the foundation of this GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP) are developed to deal with the optimization problems for stochastic systems with event-triggered impulsive controls. It is shown that the obtained controller design scheme can reduce the computational and communication burden caused by updating the controller periodically. By analyzing the admissibility, monotonicity, and optimality properties of ETIADP and HEIADP, we further establish the approximation error bound of the neural networks to address the connection between the ideal and neural-network-based realizations of the present methods. It is proven that the iterative value functions of both the ETIADP and HEIADP algorithms fall in a small neighborhood of the optimum as the iteration index increases to infinity. By adopting a novel task synchronization mechanism, the proposed HEIADP algorithm fully utilizes the computing resources of multiprocessor systems (MPSs), while significantly reducing the memory requirement compared to traditional ADP approaches. Finally, we carry out a numerical study to show that the proposed methods can fulfill the desired goals.

show abstract

Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

Cited by 10 publications

References 35 publications

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

A Dynamic Load Simulation Algorithm Based on an Inertia Simulation Predictive Model

Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP

Contact Info

Product

Resources

About