Multi-objective optimal synthesis of robust control systems for plants exhibiting non-minimum phase and integrating...

Area of Science:

• Control Systems Engineering
• Optimization Techniques
• Robotics

Background:

• Classical controller design methods struggle with complex plant dynamics, leading to suboptimal performance and limited robustness.
• Challenges include integral characteristics, non-minimum phase zeros, and time delays, affecting stability and time-domain behavior.

Purpose of the Study:

• To develop an optimal and robust controller synthesis method.
• To address limitations of classical methods by improving stability, dynamic performance, and robustness.
• To formulate controller synthesis as a multi-objective optimization problem.

Main Methods:

• Controller synthesis formulated as a multi-objective optimization problem.
• Objective function incorporates peak sensitivity, integral square error, control effort, phase margin, and delay margins.
• Multi-objective genetic algorithm used for solving the optimization problem.
• K-Means clustering and utopia point determination applied for selecting the ideal controller from Pareto-optimal solutions.

Main Results:

• The proposed controller demonstrated significant quantitative performance improvements across diverse systems (integrating, DC motor, hydropower, coupled tanks).
• Superiority over conventional methods was shown in stability, dynamic performance, and robustness metrics.
• Monte Carlo simulations confirmed the enhanced robustness of the proposed controller.

Conclusions:

• The multi-objective optimization approach offers a superior alternative to classical methods for complex control system design.
• The proposed method effectively enhances controller performance, stability, and robustness.
• The synthesis and selection methodology provides a systematic way to achieve optimal control solutions.