Optimal robust model predictive reset control design for performance improvement of uncertain linear system.

Area of Science:

• Control Engineering
• Systems Theory
• Optimization Techniques

Background:

• Uncertain linear systems pose challenges for robust control design.
• Dynamic output feedback control (DOFC) offers advantages in practical applications.
• Reset control strategies can improve system transient performance.

Purpose of the Study:

• To design a robust reset dynamic output feedback control (DOFC) for uncertain linear systems.
• To achieve closed-loop exponential stability using linear matrix inequality (LMI).
• To optimize reset times and values using genetic algorithms (GA) and model-predictive optimization.

Main Methods:

• Design of robust DOFC elements using linear matrix inequality (LMI) for stability.
• Determination of reset law, including post-reset values and constraints.
• Application of genetic algorithm (GA) for objective function minimization to find optimal reset times.
• Model-predictive-based optimization utilizing output information for reset instance tuning.

Main Results:

• Successful design of a robust reset DOFC for uncertain linear systems.
• Demonstration of closed-loop exponential stability.
• Validation of the controller's effectiveness on a distillation column and a B747 aircraft model.
• Significant improvement in transient performance compared to existing methods.

Conclusions:

• The proposed robust reset controller effectively enhances transient performance in uncertain systems.
• The combination of LMI, GA, and model-predictive optimization provides a powerful framework for robust reset control design.
• The controller shows practical applicability in complex engineering systems.