A general controller-based dynamic linearized model-free adaptive control and its application to PMLSM
Xian Yu1, Jingyu Fang1, Juanping Zhu2
1College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, PR China.
ISA Transactions
|March 1, 2026
Summary
This study enhances model-free adaptive control for unknown nonlinear systems using dynamic linearization. The data-driven approach ensures tracking error boundedness with improved mathematical analysis.
Area of Science:
- Control Engineering
- Systems Theory
- Nonlinear Dynamics
Background:
- Existing model-free adaptive control (MFAC) methods have limitations for complex systems.
- Nonlinear, non-affine discrete-time systems pose significant control challenges.
- Dynamic linearization offers a promising approach for simplifying control design.
Purpose of the Study:
- To extend and improve controller-based dynamic linearized model-free adaptive control methods.
- To develop a data-driven control approach for unknown nonlinear non-affine discrete-time systems.
- To analyze tracking error boundedness under less stringent conditions.
Main Methods:
- Full-form dynamic linearized data model and controller design.
- Simplified Newton-type optimization with adaptive estimation algorithms.
- Mathematical induction, Geršgorin discs, and contraction mapping principle for analysis.
Main Results:
- The proposed approach integrates compact and partial form models.
- Improved analysis of ultimate boundedness for tracking error.
- Demonstrated effectiveness using a permanent magnet linear synchronous motor example.
Conclusions:
- The enhanced MFAC approach is effective for unknown nonlinear systems.
- The data-driven method offers robust tracking performance.
- The approach has potential applications in various engineering fields.
Keywords:
Data-driven controlDiscrete-time systemsDynamic linearization methodModel-free adaptive controlNonlinear non-affine systemsMore Related Videos
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