Loss Minimization Control of Dual Three‐Phase Linear Induction Motor with System‐Level Loss Model

Zhou, Yingying; Zhao, Jinghong; Liu, Xiaohu

doi:10.1002/tee.23300

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…In the case of frequent system communication, errors occur in the process of data packet transmission or reception due to the competition of each node for the right to use the network bandwidth. When the data packet does not reach the receiver within the specified time, it is called packet loss [ 30 ]. When data loss occurs in the system, the system information transmission channel is temporarily disconnected.…”

Section: Methodsmentioning

confidence: 99%

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Zheng

2022

Computational Intelligence and Neuroscience

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At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

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Section: Methodsmentioning

confidence: 99%

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Zheng

2022

Computational Intelligence and Neuroscience

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Loss Minimization Model Predictive Current Control for Linear Induction Motors

Ma,

Zhao,

Yang

et al. 2024

IEEJ Transactions Elec Engng

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To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Improving the efficiency of induction motor drive by flux and torque control: A hybrid LSE-RERNN approach

Sivaraju,

Senthilkumar,

Sankar

et al. 2024

ISA Transactions

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Loss Minimization Control of Dual Three‐Phase Linear Induction Motor with System‐Level Loss Model

Cited by 4 publications

References 16 publications

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Loss Minimization Model Predictive Current Control for Linear Induction Motors

Improving the efficiency of induction motor drive by flux and torque control: A hybrid LSE-RERNN approach

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