Experimental validation of predictor-corrector approach based control schemes on the laboratory scale non-linear...

Area of Science:

• Control Engineering
• Nonlinear System Control
• Fuzzy Logic Systems

Background:

• Nonlinear systems present significant challenges for traditional control methods.
• Accurate modeling and effective control are crucial for industrial process stability.
• Existing control schemes may struggle with model uncertainties and external disturbances.

Purpose of the Study:

• To design and implement novel predictor-corrector control schemes for single-input-single-output (SISO) nonlinear systems.
• To evaluate the effectiveness of these schemes in set-point tracking and disturbance rejection.
• To validate the proposed control strategies through simulations and experimental testing.

Main Methods:

• A two-step predictor-corrector approach was developed.
• The first step utilizes nonlinear or fuzzy-weighted linear models for initial controller output calculation.
• The second step refines the controller output based on real-time measurements.

Main Results:

• Extensive simulations confirmed satisfactory set-point tracking and disturbance rejection capabilities.
• The control schemes performed well in both the absence and presence of model-plant mismatch.
• Performance was superior or comparable to a gain-scheduled Proportional-Integral (PI) controller.

Conclusions:

• The proposed predictor-corrector control schemes are effective for nonlinear SISO systems.
• Experimental validation on a conical tank setup confirms practical applicability.
• These methods offer a robust alternative to traditional control strategies for complex systems.