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Model-Free Cooperative Adaptive Sliding-Mode-Constrained-Control for Multiple Linear Induction Traction Systems.

Dezhi Xu, Weiming Zhang, Peng Shi

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    A new model-free control strategy addresses speed control in multiple linear induction motor systems. This adaptive sliding-mode approach handles constraints, preventing actuator saturation for improved system performance.

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    Area of Science:

    • Control Systems Engineering
    • Electrical Engineering
    • Robotics

    Background:

    • Multiple linear induction motor systems face challenges with speed cooperative control.
    • Input magnitude and rate constraints can lead to actuator and integral saturation, degrading performance.

    Purpose of the Study:

    • To propose a model-free cooperative adaptive sliding-mode-constrained-control strategy for multiple linear induction traction systems.
    • To address actuator and integral saturation issues caused by input constraints.

    Main Methods:

    • Investigated the equivalent circuit topology of individual motors.
    • Modeled the system as a multiagent system with fixed communication topology.
    • Developed an output observer for state estimation and an algorithm for pseudo-partial derivative parameter and uncertainty estimation.
    • Designed an integral sliding-mode surface with an anti-windup compensator to manage systematic error and saturation.

    Main Results:

    • Simulations demonstrated the effectiveness of the proposed control strategy.
    • The strategy successfully handled input constraints and prevented actuator saturation.
    • The cooperative control approach showed superiority in performance for the multiagent system.

    Conclusions:

    • The proposed model-free cooperative adaptive sliding-mode-constrained-control strategy is effective for multiple linear induction traction systems.
    • The strategy successfully mitigates saturation issues and enhances cooperative control performance.
    • This approach offers a robust solution for complex traction system control.