Futuristic crane smoothly lifting a glowing payload with precision.

Crane Control Revolution: How Smart Algorithms are Preventing Accidents

Lena Kashyap in Tech & Innovation July 2025 • 3 min read.

"Discover how Dynamic Differential Evolution algorithms are optimizing sliding mode controllers to make under-actuated cranes safer and more efficient."

Cranes are the unsung heroes of construction sites, harbors, and industrial factories, tirelessly lifting and moving heavy loads. Under-actuated cranes, known for their wide application in heavy cargo transportation, present unique challenges. Unlike fully-actuated systems, these cranes have fewer control inputs than degrees of freedom, making precise control a complex task.

One of the most critical issues is payload oscillation. The high-speed movement of the trolley can easily cause the payload to swing, leading to potential collisions and safety hazards. As such, designing effective anti-swing controllers is paramount.

The need for innovation in crane control is driving research toward smarter, more adaptive systems. In a groundbreaking study published in IEEE Access in September 2018, researchers Zhe Sun, Xuejian Zhao, Zhixin Sun, Feng Xiang, and Chunjing Mao introduced an optimal sliding mode controller design based on a Dynamic Differential Evolutionary (DDE) algorithm for under-actuated crane systems.

The Innovative DDE-SMC Solution

Futuristic crane smoothly lifting a glowing payload with precision.

The core of this innovation lies in the Dynamic Differential Evolution algorithm-based sliding-mode controller (DDE-SMC). This method aims to tackle the residual vibration problem in under-actuated crane systems with a novel approach.

The key innovations:

Fusion Sliding Function: Combines position and angle sliding functions to provide a comprehensive control strategy.
Switching and Equivalent Control Law: Designed to ensure precise control throughout the lifting process.
Dynamic Differential Evolution (DDE) Algorithm: Optimizes control parameters to enhance anti-swing performance.

The DDE algorithm optimizes control parameters to improve the anti-swing control performance effectively. To validate the algorithm's robustness, the team conducted computer simulations under various operational conditions, demonstrating the effectiveness of the DDE-SMC in damping payload oscillations in under-actuated crane systems.

Future of Crane Systems

The potential of DDE-SMC extends beyond just reducing accidents. By improving the efficiency and precision of crane operations, industries can see increased productivity, reduced material waste, and safer working conditions. As AI and machine learning continue to evolve, expect even more sophisticated solutions that make workplaces safer and more efficient.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/access.2018.2872062, Alternate LINK

Title: Optimal Sliding Mode Controller Design Based On Dynamic Differential Evolutionary Algorithm For Under-Actuated Crane Systems

Subject: General Engineering

Journal: IEEE Access

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Authors: Zhe Sun, Xuejian Zhao, Zhixin Sun, Feng Xiang, Chunjing Mao

Published: 2018-01-01

Everything You Need To Know

What makes under-actuated cranes challenging to control?

Under-actuated cranes are unique because they have fewer control inputs than degrees of freedom. This makes it difficult to precisely control the movement, especially in preventing payload oscillation. High-speed trolley movements can cause the load to swing, posing safety risks and potential collisions. Effective anti-swing controllers are crucial to address these challenges and ensure safer crane operations.

What is the Dynamic Differential Evolution algorithm-based sliding-mode controller (DDE-SMC), and how does it address vibration issues in crane systems?

The Dynamic Differential Evolution algorithm-based sliding-mode controller (DDE-SMC) tackles residual vibration in under-actuated crane systems. It utilizes a fusion sliding function (combining position and angle), switching and equivalent control law for precise control, and the DDE algorithm to optimize control parameters. These components work together to enhance anti-swing performance, validated through computer simulations under various operational conditions.

How does the Dynamic Differential Evolution (DDE) algorithm optimize the control parameters within the DDE-SMC, and why is this optimization so important?

The Dynamic Differential Evolution (DDE) algorithm optimizes control parameters within the DDE-SMC to improve anti-swing control performance. By dynamically adjusting these parameters, the algorithm enhances the crane's ability to dampen payload oscillations effectively. This optimization is crucial for maintaining stability and preventing accidents during crane operations. Further research areas could explore how real-time data and machine learning can further enhance DDE parameter optimization for even more adaptive crane control.

What does the 'fusion sliding function' do in the DDE-SMC, and why is it important to crane control?

The fusion sliding function combines position and angle sliding functions to provide a comprehensive control strategy for under-actuated cranes. This integrated approach allows the system to monitor and adjust both the position and the angle of the payload, ensuring precise and coordinated control throughout the lifting process. Without considering both aspects, control would be incomplete, potentially leading to instability.

What are the potential benefits of using DDE-SMC in crane systems, and how might future technologies enhance these advantages?

The implementation of DDE-SMC offers several advantages, including reduced accidents due to enhanced anti-swing control, increased productivity from more efficient crane operations, reduced material waste through improved precision, and safer working conditions. As AI and machine learning continue to advance, integrating these technologies with DDE-SMC could lead to even more sophisticated and adaptive crane systems, further improving safety and efficiency in industrial settings.