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Published (Version of Record)CC BY V4.0, Open Access
Journal article
Direct thrust force control of primary permanent magnet linear motor based on improved extended state observer and model-free adaptive predictive control
Actuators, Vol.11(10), Article 270
2022
Abstract
A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control.
Details
- Title
- Direct thrust force control of primary permanent magnet linear motor based on improved extended state observer and model-free adaptive predictive control
- Authors/Creators
- X. Wang (Author/Creator)S. Yao (Author/Creator)C. Qu (Author/Creator)Y. Wang (Author/Creator)Z. Xu (Author/Creator)W. Huang (Author/Creator)H. Wang (Author/Creator)
- Publication Details
- Actuators, Vol.11(10), Article 270
- Publisher
- MDPI
- Identifiers
- 991005545305107891
- Copyright
- © 2022 by the authors
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Industry collaboration
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- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.18 Power Systems & Electric Vehicles
- 4.18.136 Electric Motor Control
- Web Of Science research areas
- Engineering, Mechanical
- Instruments & Instrumentation
- ESI research areas
- Engineering