Estimation and Identification of Nonlinear Parameter of Motion Index Based on Least Squares Algorithm

Area of Science:

• Automatic Control
• System Identification
• Nonlinear Systems

Background:

• Parameter identification is crucial for modeling complex and ill-defined systems.
• Increasing system scale leads to higher computational demands in identification algorithms.
• Over-parameterized methods for nonlinear systems with parameter products significantly increase complexity.

Purpose of the Study:

• To explore a parameter estimation method with reduced computational requirements.
• To address the computational burden associated with large-scale and complex control systems.
• To improve the accuracy of parameter identification in challenging system models.

Main Methods:

• Development of a computationally efficient parameter estimation algorithm.
• Application to nonlinear systems, particularly those with products of unknown parameters.
• Focus on overcoming limitations like data saturation in identification processes.

Main Results:

• The proposed method significantly reduces the calculation amount compared to traditional approaches.
• Demonstrated ability to overcome the phenomenon of data saturation.
• Achieved improved parameter identification results for complex nonlinear systems.

Conclusions:

• The developed method offers a computationally feasible solution for parameter identification.
• Effective for complex nonlinear systems, enhancing model accuracy and reliability.
• Provides a valuable alternative to computationally intensive over-parameterized identification techniques.