An Improved Parameter Identification Algorithm for the Friction Model of Electro-Hydraulic Servo Systems

Area of Science:

• Control Systems Engineering
• Mechanical Engineering
• Computational Intelligence

Background:

• Friction in electro-hydraulic servo systems causes performance degradation, including creeping and jitter.
• Accurate friction modeling is crucial for effective compensation and improved system precision.
• Traditional genetic algorithms struggle with premature convergence and accuracy in parameter identification.

Purpose of the Study:

• To develop an improved adaptive genetic identification algorithm for LuGre friction model parameters.
• To enhance the accuracy and convergence speed of parameter identification in electro-hydraulic servo systems.
• To overcome the limitations of traditional genetic algorithms in friction compensation.

Main Methods:

• Utilized the LuGre friction model to characterize servo system friction dynamics.
• Developed an improved adaptive genetic algorithm with adaptive evolutionary process selection.
• Adjusted crossover and mutation probabilities dynamically based on population concentration.
• Reduced the parameter search range in later evolutionary stages for improved accuracy.

Main Results:

• The proposed algorithm achieved a relative error of less than 1% in model parameter identification.
• Demonstrated significantly faster convergence speed compared to traditional and existing adaptive genetic algorithms.
• Simulation results confirmed superior overall performance of the proposed identification method.

Conclusions:

• The improved adaptive genetic algorithm offers a highly accurate and efficient method for friction model parameter identification.
• This approach effectively addresses limitations of conventional methods, enhancing electro-hydraulic servo system performance.
• Provides a feasible solution for precise friction compensation in demanding control applications.