Experimental studies on model reference adaptive control with integral action employing a rotary encoder and...

Area of Science:

• Control Systems Engineering
• Robotics and Automation
• Electrical Engineering

Background:

• DC motors are crucial actuators in various automated systems.
• Precise control of DC motor position and velocity is essential for performance.
• Existing adaptive control methods may struggle with integral terms and external disturbances.

Purpose of the Study:

• To design and implement an adaptive control law with integral action for a DC motor.
• To enhance tracking performance and stability using Lyapunov functions and Barbalat's lemma.
• To validate the control strategy's effectiveness through experimental results.

Main Methods:

• Development of an adaptive control law incorporating an integral action.
• Utilization of rotary encoder and tachometer sensors for feedback.
• Stability analysis using Lyapunov functions and convergence proof via Barbalat's lemma.
• Specification of performance using a reference model and weighting matrices (Q and Γ).

Main Results:

• The proposed adaptive control law ensures asymptotic convergence of tracking errors to zero.
• Experimental results show a significant reduction in maximum position error from 0.4 V to 0.2 V.
• Maximum velocity error was reduced from 1.5 V to 0.4 V with the integral action.
• The control system demonstrated robust performance under input disturbances.

Conclusions:

• The adaptive control law with integral action effectively improves DC motor control performance.
• The method provides enhanced stability and superior tracking accuracy for position and velocity.
• The proposed control strategy is resilient to input disturbances, offering reliable operation.