An improved sampled-data control for a nonlinear dynamic positioning ship with Takagi-Sugeno fuzzy model

Area of Science:

• Marine Engineering
• Control Systems Theory
• Fuzzy Logic Systems

Background:

• Dynamic positioning systems (DPS) are crucial for marine vessels.
• Sampled-data control is essential for modern control systems with discrete-time information.
• Takagi-Sugeno (T-S) fuzzy models offer a framework for nonlinear system representation.

Purpose of the Study:

• To develop an improved sampled-data controller for dynamic positioning ships (DPS) using Takagi-Sugeno (T-S) fuzzy models.
• To reduce conservatism in stability analysis and controller design for DPS.
• To ensure robust performance and address mode uncertainties in the control system.

Main Methods:

• Construction of an improved Lyapunov-Krasovskii function (LKF) incorporating new terms.
• Application of the reciprocally convex method to bound the derivative of the LKF.
• Utilization of linear matrix inequality (LMI) for stability conditions, robust performance, and controller design.

Main Results:

• The proposed LKF effectively utilizes sampling information throughout the entire sampling period.
• Less conservatism was achieved in the stability analysis and controller design.
• The method demonstrated effectiveness in analyzing stability conditions, robust performance, and mode uncertainties.

Conclusions:

• The developed sampled-data control method offers improved performance and reduced conservatism for DPS.
• The approach effectively handles mode uncertainties and ensures robust performance.
• The LMI-based analysis provides a systematic way to design controllers for T-S fuzzy systems in DPS applications.