<p>To enhance the control performance of permanent magnet linear synchronous motor servo system, this paper proposes an adaptive fractional-order predefined performance control method. Initially, a prescribed performance function is designed without requiring exact initial error, transforming the position tracking error into a transformation error. This transformation ensures that the position tracking error remains within predefined boundaries by controlling the boundedness of the transformation error, thereby enabling quantitative design of both steady-state and transient performance of the control system. Subsequently, barrier function-based adaptive control is introduced to estimate the upper bound of uncertainty disturbances in real time, eliminating the need for a priori information about such disturbances. This approach effectively mitigates the overestimation of control gain and reduces chattering phenomena. Furthermore, the steady-state error of the system is minimized through the application of fractional-order sliding mode techniques. The boundedness of the transformation error and the stability of the closed-loop system are demonstrated using the Lyapunov function method. Finally, the effectiveness and superiority of the proposed control method are validated on a cSPACE-based PMLSM platform.</p>

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Practical tracking control of permanent magnet linear servo system with adaptive fractional-order predefined performance control

  • Xinyu Zhao,
  • Limei Wang

摘要

To enhance the control performance of permanent magnet linear synchronous motor servo system, this paper proposes an adaptive fractional-order predefined performance control method. Initially, a prescribed performance function is designed without requiring exact initial error, transforming the position tracking error into a transformation error. This transformation ensures that the position tracking error remains within predefined boundaries by controlling the boundedness of the transformation error, thereby enabling quantitative design of both steady-state and transient performance of the control system. Subsequently, barrier function-based adaptive control is introduced to estimate the upper bound of uncertainty disturbances in real time, eliminating the need for a priori information about such disturbances. This approach effectively mitigates the overestimation of control gain and reduces chattering phenomena. Furthermore, the steady-state error of the system is minimized through the application of fractional-order sliding mode techniques. The boundedness of the transformation error and the stability of the closed-loop system are demonstrated using the Lyapunov function method. Finally, the effectiveness and superiority of the proposed control method are validated on a cSPACE-based PMLSM platform.