An approximate model matching technique for controller design of linear time-invariant systems using hybrid...

Area of Science:

• Control Systems Engineering
• Metaheuristic Optimization
• Signal Processing

Background:

• Traditional control system design often involves complex models and high-order controllers.
• Implementing controllers with unknown parameters requires robust and adaptable methods.
• Existing metaheuristic algorithms may lack efficiency in complex control system synthesis.

Purpose of the Study:

• To develop a novel delta operator-based intelligent control scheme.
• To utilize new firefly-based hybrid metaheuristic algorithms for controller design.
• To establish a systematic approach for synthesizing low-order, output-feedback controllers.

Main Methods:

• Approximate model matching using a reference model and PID controller.
• Development of novel firefly-based hybrid metaheuristic algorithms.
• Controller synthesis in the unified delta operator domain.
• Comparison with continuous-time domain parameter estimation.

Main Results:

• The proposed method yields rational, lower-order controllers implementable with output feedback.
• Estimated controller parameters in the delta operator domain closely match continuous-time results.
• Validation through application to several well-known plant models.
• Demonstrated percentage improvement over parent algorithms and other existing techniques.

Conclusions:

• A consolidated and effective approach to controller synthesis is established.
• The delta operator-based intelligent control scheme is applicable to a wide range of plant models, including unstable and non-minimum-phase systems.
• The choice of reference model can enhance the applicability of the control scheme.