Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence in Engineering
• Mechatronics

Background:

• Linear motion single-axis robot machines are susceptible to various uncertainty disturbances affecting precision.
• Existing control systems struggle to effectively mitigate lumped uncertainties like external forces, friction, and parameter variations.
• High-precision control is crucial for applications demanding micrometer-level accuracy.

Purpose of the Study:

• To develop a micrometer backstepping control system for a linear motion single-axis robot drive system.
• To estimate and compensate for lumped uncertainties using an amended recurrent Gottlieb polynomials neural network (NN) and an altered ant colony optimization (AACO).
• To achieve high-precision control performance with micrometer resolution.

Main Methods:

• Implementation of a digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme.
• Development of a micrometer backstepping control system incorporating an amended recurrent Gottlieb polynomials NN with a compensated controller.
• Utilization of a Lyapunov function-based adaptive law for the NN to estimate lumped uncertainty.
• Proposal of a novel error-estimated law for the compensated controller to address estimation errors.
• Application of AACO to optimize NN learning rates for faster convergence.

Main Results:

• Successfully applied DSP-based PWM control to the robot drive system.
• The proposed backstepping control system effectively diminished lumped uncertainty effects.
• High-precision control performance was achieved through accurate uncertainty estimation via the NN's adaptive law.
• The compensated controller successfully mitigated estimation errors.
• AACO accelerated the NN's convergent speed by regulating learning rates.

Conclusions:

• The proposed micrometer backstepping control system, integrating an amended NN and AACO, significantly enhances the precision of linear motion single-axis robot machines.
• The adaptive NN and compensated controller effectively handle lumped uncertainties, leading to superior control performance.
• Experimental validation confirms the effectiveness and robustness of the developed control scheme for high-precision applications.