Sand cat swarm optimization-based feedback controller design for nonlinear systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Controlling open-loop unstable systems with nonlinear structures presents significant challenges.
• Existing metaheuristic algorithms may not always provide optimal solutions efficiently.

Purpose of the Study:

• To propose a novel Sand Cat Swarm Optimization (SCSO) algorithm for state feedback controller design.
• To address the control challenges in open-loop unstable nonlinear systems.

Main Methods:

• Development of a state feedback controller utilizing the Sand Cat Swarm Optimization (SCSO) algorithm.
• Application and simulation of the proposed controller on nonlinear systems: Inverted Pendulum, Furuta Pendulum, and Acrobat Robot Arm.
• Comparison of SCSO algorithm's performance against other established metaheuristic algorithms.

Main Results:

• The SCSO-based controller successfully optimized control parameters with rapid convergence.
• The proposed method demonstrated superior or competitive performance compared to other metaheuristic algorithms.
• Effective control of nonlinear unstable systems was achieved.

Conclusions:

• The Sand Cat Swarm Optimization algorithm offers an efficient and effective approach for designing state feedback controllers for nonlinear unstable systems.
• The proposed SCSO-based controller shows significant potential for real-world control applications.