Tuning of Smith predictor based generalized ADRC for time-delayed processes via IMC.

Area of Science:

• Control Systems Engineering
• Automation and Process Control
• Mechatronics and Robotics

Background:

• Time-delayed systems present significant control challenges due to inherent system lag.
• Existing control strategies often struggle to balance performance and robustness in the presence of delays.
• Active disturbance rejection control (ADRC) and Smith Predictors (SP) offer complementary strengths for handling system uncertainties and delays.

Purpose of the Study:

• To develop and investigate a hybrid control strategy for time-delayed systems by integrating Generalized Active Disturbance Rejection Control (GADRC) with a Smith Predictor (SP).
• To leverage the advantages of both GADRC and SP to create a robust and effective control solution for systems with significant time delays.
• To provide a clear analytical framework and tuning guidelines for the proposed SP-GADRC controller.

Main Methods:

• A novel control strategy, SP-GADRC, is proposed by combining GADRC with an SP to account for both plant dynamics and time delays.
• The SP-GADRC controller is reformulated into an equivalent two-degree-of-freedom internal model control (TDF-IMC) structure for simplified interpretation and tuning.
• Tuning parameters for the SP-GADRC controller, specifically the controller bandwidth and Extended State Observer (ESO) bandwidth, are mapped to equivalent time constants within the TDF-IMC framework.

Main Results:

• The proposed SP-GADRC strategy effectively utilizes all available model information, including plant dynamics and time delay.
• The transformation to a TDF-IMC structure simplifies the analysis and tuning process, offering practical guidance for engineers.
• Comparative simulations demonstrate the effectiveness and superior performance of the SP-GADRC compared to existing methods.

Conclusions:

• The SP-GADRC offers a powerful and practical approach for controlling time-delayed systems.
• The TDF-IMC interpretation provides valuable insights for controller design and implementation in real-world applications.
• This control strategy enhances system stability and performance, making it suitable for various engineering disciplines.