Real-Time SIL Validation of a Novel PMSM Control Based on Deep Deterministic Policy Gradient Scheme for Electrified Vehicles

Bhattacharjee, Soumava; Halder, Sukanta; Yan, Ye; Balamurali, Aiswarya; Iyer, Lakshmi Varaha; Kar, Narayan C.

doi:10.1109/tpel.2022.3153845

Cited by 17 publications

(4 citation statements)

References 26 publications

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“…The two current and speed controllers are the main components of the PMSM drive system, as they are the brain for the driving inverter. The machine dynamics and transient performance are highly dependent on the performance of these two controllers [7]. In the APIbased control approach, within both current and speed control loops, the input of each PI control block, parameters of the PMSM under study and the power inverter are used to tune the API controllers as control block are the error signals, which are calculated by subtracting the command and feedback values.…”

Section: B Api Design and Structurementioning

confidence: 99%

“…To counter these uncertainties, various adaptive control strategies has been devised. Among these, techniques such as neural network-based control [1], proportional-integral (PI) with proportional-resonant (PR) control [2], model predictive control (MPC) [3], direct torque control (DTC) [4], fuzzy logic controllers (FLC) [5], [6], deep reinforcement learning (DRL)-based control [7], and mixed H2/H∞ control [8]- [10] have emerged. Despite their innovative approaches, each method encounters specific obstacles, highlighting the ongoing struggle to balance novelty, efficiency, and practicality in control system design.…”

Section: Introductionmentioning

confidence: 99%

“…To mitigate the impact of disturbances, [7] suggests a speed and current controller based on DRL. The complexity and computational requirements associated with DRL, however, are significant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Validation of Enhanced Permanent Magnet Synchronous Motor Drive Using Dense-Neural-Network-Based Control

Fatemimoghadam,

Varaha Iyer,

Kar

2024

IEEE Access

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High-performance current and speed control are required to obtain smooth output torque, current tracking, and speed tracking in permanent-magnet synchronous motor (PMSM) drives. The motor speed and stator current control rely on multiple nonlinear motor parameters, which play a crucial role in shaping the performance of PMSM. Moreover, tuning the speed and current controller parameters using the conventional control technique depends on these PMSM parameters, also variation of these parameters will have a decisive influence on the dynamic performance of PMSM. To enhance the robustness of vector control and tracking methodology against PMSM parameter uncertainties and load disturbances, a novel artificial intelligence (AI)-based advanced speed and current control technique for PMSM is proposed in this article. Subsequently, the methodology for designing and training the suggested Dense Neural Network (DNN) controllers are elicited. The proposed controllers can handle the inevitable fluctuation and non-linearity in motor parameters at different load points and drive conditions. The proposed DNN scheme is validated in terms of settling time, dynamic responsiveness, tolerance to parameter fluctuations, and overall robustness. A comparative analysis is conducted against adaptive proportional-integral (API) control applied to the same PMSM within the OPAL-RT real-time simulator (RTS). The viability of the proposed control scheme is substantiated through simulation, Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) testing with an RTS and an automotive-grade controller board across diverse conditions.

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Section: B Api Design and Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Validation of Enhanced Permanent Magnet Synchronous Motor Drive Using Dense-Neural-Network-Based Control

Fatemimoghadam,

Varaha Iyer,

Kar

2024

IEEE Access

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“…The torque ripple generated by the engine at the side of driving plate can also have an adverse effect during the process of clutch engagement. Despite the short duration of the slipping phase, the nonlinear variation in torque will intensify the mismatching torque in the vehicle powertrain, leading to an obvious unpleasant driving sensation [20][21][22][23]. Hence, further improvement in the drivability and smoothness of HEVs during mode transition remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Coordinated Control Strategy for Parallel Hybrid Electric Vehicles during Clutch Slipping Process

Tian

Wang

et al. 2022

Applied Sciences

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For parallel hybrid electric vehicles (HEVs), the clutch serves as a vital enabling actuator element during mode transitions. The expected drivability and smoothness of parallel HEVs are difficult to be achieve owing to the neglect of clutch-torque-induced disturbance and different response characteristics of power sources during clutch slipping. To address this issue, this paper proposes a novel control strategy to coordinate the engine and motor during the clutch slipping process. A sliding mode control strategy based on a group-preserving scheme was applied to control the motor. The vehicle dynamic equation was constructed by the sliding surface with the Lagrange function. The equation solutions obtained by introducing the Runge–Kutta method were used as motor control inputs. Meanwhile, an adaptive PI controller was designed to regulate engine speed for the reduction in the speed difference of the clutch. The hardware-in-the-loop simulations were conducted to validate the outstanding performance of the proposal strategy. The verification results indicate that the proposed strategy not only reduces the vehicle jerk and frictional losses effectively, but also improves vehicle driving comfort and reliability.

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Motor Position Calibration Method Based on Sinusoidal Feedback Signal Envelope

Jiang,

Zeng

2024

Lecture Notes in Electrical Engineering

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Real-Time SIL Validation of a Novel PMSM Control Based on Deep Deterministic Policy Gradient Scheme for Electrified Vehicles

Cited by 17 publications

References 26 publications

Real-Time Validation of Enhanced Permanent Magnet Synchronous Motor Drive Using Dense-Neural-Network-Based Control

Real-Time Validation of Enhanced Permanent Magnet Synchronous Motor Drive Using Dense-Neural-Network-Based Control

A Novel Coordinated Control Strategy for Parallel Hybrid Electric Vehicles during Clutch Slipping Process

Motor Position Calibration Method Based on Sinusoidal Feedback Signal Envelope

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