Experimental verification of a hybrid control scheme with chaotic whale optimization algorithm for nonlinear gantry...

Area of Science:

• Control Systems Engineering
• Robotics
• Optimization Algorithms

Background:

• Gantry cranes (GC) are critical in logistics but suffer from payload sway, impacting efficiency and safety.
• Existing control methods often struggle with the inherent nonlinear dynamics of GCs.
• Advanced control strategies are needed for precise and stable GC operation.

Purpose of the Study:

• To experimentally validate a novel hybrid control scheme for a nonlinear gantry crane system.
• To assess the effectiveness of partial feedback linearization (PFL) and deadbeat (DB) control combined with chaotic whale optimization algorithm (CWOA) for sway reduction.
• To demonstrate the superiority of the proposed scheme through comparative analysis.

Main Methods:

• Implementation of a hybrid control strategy combining PFL for linearization and DB control for accelerated response.
• Utilization of chaotic whale optimization algorithm (CWOA) for optimal tuning of controller parameters.
• Employing a sliding-mode observer (SMO) for real-time estimation of unmeasured system states.

Main Results:

• The proposed hybrid PFL-DB control scheme significantly reduces payload sway in the gantry crane system.
• Experimental results confirm the effectiveness of CWOA in optimizing controller parameters for improved performance.
• The integrated SMO accurately estimates unmeasured states, contributing to overall system stability.

Conclusions:

• The experimentally verified hybrid control scheme offers a robust and effective solution for controlling nonlinear gantry cranes.
• The proposed method provides superior payload sway elimination compared to conventional approaches.
• This research highlights the potential of hybrid control and metaheuristic optimization in complex robotic systems.