Iterative Learning Algorithm with Adaptive Forgetting Function for Suppressing Compressor Torque Ripple
- Resource Type
- Conference
- Authors
- Yang, Zhebin; Deng, Rongfeng; Yang, Jiaqiang; Gu, Tangtang; Zhuo, Senqing; Li, Fashun
- Source
- 2023 IEEE 6th Student Conference on Electric Machines and Systems (SCEMS) Electric Machines and Systems (SCEMS), 2023 IEEE 6th Student Conference on. :1-7 Dec, 2023
- Subject
- Power, Energy and Industry Applications
Air conditioning
Torque
Adaptive systems
Electric machines
Observers
Control systems
Iterative algorithms
Compressor system
Low frequency torque ripple
Forgetting function
Adaptive function
Reduced-order state observer
Iterative learning
- Language
- ISSN
- 2771-7577
Torque ripple is an important concern in air conditioning compressor systems operating at low frequencies. An algorithm for suppressing compressor torque ripple is proposed to address the problems of significant torque ripple. This iterative learning algorithm with an adaptive forgetting function is applied to the speed loop, improving the learning ability under different operating frequencies. The optimal parameter configurations are achieved by analyzing control system discretization. Meanwhile, A reduced-order state observer is included to eliminate non-periodic disturbances and enhance system stability. And the output of the proposed algorithm is compensated to the given current in the q-axis The algorithm is validated on an experimental platform for air conditioning compressors, Experimental results show that the algorithm can significantly reduce the compressor torque ripple.