Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification

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Abstract
To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes.
Funding Information
  • National Natural Science Foundation of China (52477095)

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