Control of Permanent Magnet Synchronous Motors for Track Applications

Mercorelli, Paolo

doi:10.3390/electronics12153285

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…Neglecting the inhomogeneity of the air gap of the surface-mounted PMSM, where L d = L q = L.T e is the electromagnetic torque, T L is the load torque, B is the damping factor, and J is the rotational inertia. Equation (3) can be deduced from Equations ( 1) and (2).…”

Section: Pmsm Mathematical Modelmentioning

confidence: 99%

“…Permanent Magnet Synchronous Motor (PMSM) is a high-performance motor type widely used because of its high efficiency, low noise, long life, etc. [1][2][3]. Conventional PMSM speed controllers usually use a cascaded PID control strategy, where an outerloop speed controller and an inner-loop current controller act together to stabilize the desired dynamic error [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Research on PMSM Speed Performance Based on Fractional Order Adaptive Fuzzy Backstepping Control

Zhang,

Ma,

et al. 2023

Energies

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A permanent magnet synchronous motor (PMSM) is a nonlinear, strongly coupled, controlled object with time-varying, fractional-order characteristics. It is difficult to achieve the ideal control effect by using the traditional control method when motor parameter changes and load perturbations occur during the operation of the PMSM, so a fractional-order adaptive fuzzy backstepping control method is proposed to improve the system’s fast response and anti-jamming ability in the case of sudden changes in rotational speed, load perturbations and other conditions. Initially, the fractional order theory is introduced, backstepping control is utilized to decompose the system into multiple subsystems, and a fractional order-based Lyapunov function is designed for each subsystem to ensure the system’s stability. Suitable control laws, as well as parameter adaptive laws, are derived through rigorous mathematical derivation. Finally, a fractional order adaptive fuzzy backstepping controller (FOAB-FPID) is designed by combining the advantages of fuzzy control. Then a mechanical simulation model of the PMSM is established to verify the validity of the designed controller, followed by three sets of comparative experiments: PID, fuzzy PID (F-PID), and integer-order adaptive fuzzy backstepping (IOAB-FPID), which are selected to simulate the PMSM under the control of the four controllers. Finally, it is validated on the constructed PMSM experimental platform. Simulation and experimental results show that FOAB-FPID can adaptively adjust system parameters during sudden speed changes, achieve real-time speed tracking, and maintain speed stability under load perturbations and internal parameter uptake. Compared with the three control strategies, reached PMSM system has better acceleration, fast response performance, and better anti-disturbance ability, which proves the rationality and effectiveness of the FOAB-FPID control method.

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Section: Pmsm Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on PMSM Speed Performance Based on Fractional Order Adaptive Fuzzy Backstepping Control

Zhang,

Ma,

et al. 2023

Energies

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“…However, from year to year, the prices of these circuits are becoming lower. In the near future, the vector methods may become the standard methods for controlling BLDC and PMSM motors [23].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Computational Accuracy on the Quality of Direct Drive Control

Góra,

Petko,

Gac

et al. 2024

Electronics

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One of the main aspects of the control system development process for direct drives is the selection of the required computational accuracy while establishing its impact on the quality of the control. Understanding this relationship allows designers to consciously determine the system structure at the early stages of controller development, which enables the optimal usage of hardware resources. This paper analyzes the results of experimental research on the influence of computational accuracy on the quality of control of direct drives. During the carried-out research, several vector controllers with different computational precision were implemented using Field-Programmable Gate Arrays (FPGAs). The experiments were carried out on a dedicated research stand for testing direct drives. Test scenarios included position and trajectory monitoring under various torque loads. To assess the control quality, the measures based on the deviations from the value set by the controller were proposed. In this paper, the results of experiments have been presented in the form of values of the measures in relation to the computational accuracy. The obtained results proved that satisfactory drive operation parameters can be obtained despite the relatively low accuracy of calculations in the control algorithm.

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“…As a result, this study has focused on enhancing the resilience and dynamic response of PMSM drive motors. Cascade control structures, featuring internal current loops and an external speed control loop, are commonly utilized to enhance responsiveness and disturbance handling, thereby improving overall control effectiveness [2]. Despite the benefits of electric vehicles such as zero emissions and efficiency at low speeds, they encounter hurdles related to battery charging time, high power demands for high-speed driving, and extreme temperature conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

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This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.

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Control of Permanent Magnet Synchronous Motors for Track Applications

Cited by 8 publications

References 45 publications

Research on PMSM Speed Performance Based on Fractional Order Adaptive Fuzzy Backstepping Control

Research on PMSM Speed Performance Based on Fractional Order Adaptive Fuzzy Backstepping Control

The Impact of Computational Accuracy on the Quality of Direct Drive Control

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

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