A loss minimization control method for IPMSM drive system based on improved gradient descent algorithm

Yang, Hongyi; Huang, Xiaoyan; Shen, Qidi; Li, Zhaokai; Wu, Min

doi:10.1587/elex.19.20220069

Cited by 3 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The loss analysis of the PMSM is a challenging problem. Simply put, motor losses are mainly composed of copper losses and iron losses, which have been extensively discussed in numerous papers [ 27 , 28 , 29 ]. However, the actual motor losses are influenced by more complex factors.…”

Section: Cost Functions Of Mpdmcmentioning

confidence: 99%

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling

Gao

Chai

2023

Sensors

View full text Add to dashboard Cite

This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle’s dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver’s commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., “Nor”, “Eco”, and “Spt”. Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance.

show abstract

Section: Cost Functions Of Mpdmcmentioning

confidence: 99%

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling

Gao

Chai

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Permanent magnet synchronous motor (PMSM) has become an essential part of automobiles, robots, aviation, new energy, and other fields due to its high power density, operating efficiency, and other advantages [1][2][3][4][5]. A PMSM can be treated as a multivariable strong coupling system.…”

Section: Introductionmentioning

confidence: 99%

Vector control of permanent magnet synchronous motor drive system based on new sliding mode control

Zhang,

Wu,

Chien

et al. 2023

IEICE Electron. Express

View full text Add to dashboard Cite

A new sliding mode speed controller (NSMC) based on an improved genetic algorithm (IGA) is proposed to solve the problems of disturbance rejection and response speed differences in traditional vector control of permanent magnet synchronous motor (PMSM) driver systems. The improved algorithm adopts an adaptive crossover and mutation probability formula, which enhances the global search ability of the genetic algorithm. The algorithm is used to optimize the parameters of the sliding mode speed controller. Moreover, the sliding mode disturbance observer is used to generate feedforward signals to compensate for the influence of external disturbances. It is applied to the speed control loop to effectively improve the system's robustness. Finally, numerical simulation results demonstrate the robustness and fast response of the proposed method.

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

A loss minimization control method for IPMSM drive system based on improved gradient descent algorithm

Cited by 3 publications

References 27 publications

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling

Vector control of permanent magnet synchronous motor drive system based on new sliding mode control

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

Contact Info

Product

Resources

About