Self-tuning control of systems with unknown time delay via extended polynomial identification

Area of Science:

• Control Systems Engineering
• Automation and Process Control
• Adaptive Control Theory

Background:

• Existing self-tuning control methods primarily focus on systems with known time delays.
• Real-world industrial processes often exhibit unknown or time-varying delays, posing challenges for conventional controllers.
• Accurate estimation of system parameters and time delays is crucial for effective adaptive control.

Purpose of the Study:

• To extend the continuous-time self-tuning control algorithm to systems with unknown or time-varying time delays.
• To develop a modified polynomial identification method capable of estimating system poles, zeros, and unknown time delay.
• To design and evaluate an explicit self-tuner based on the estimated parameters.

Main Methods:

• Modification of the original polynomial identification technique to incorporate time delay estimation.
• Development of an explicit self-tuning controller utilizing the estimated system parameters and time delay.
• Experimental validation of the proposed algorithm's performance on relevant systems.

Main Results:

• The modified polynomial identification effectively estimates system poles, zeros, and unknown time delays.
• The explicit self-tuner, designed with estimated parameters, demonstrates robust performance.
• Experimental studies confirm the algorithm's capability in handling systems with unknown or varying time delays.

Conclusions:

• The proposed extension of the self-tuning control algorithm successfully addresses systems with unknown or varying time delays.
• The enhanced identification method provides accurate parameter and time delay estimation, crucial for adaptive control.
• The developed explicit self-tuner offers a viable solution for improving control performance in complex industrial applications.