Quick Takeaways
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Innovative Controller Development: Researchers, led by Dr. Van-Truong Nguyen, have created a novel adaptive nonlinear PID (NPID) controller integrated with a radial basis function neural network (RBFNN) for ballbots, enhancing mobility control while addressing stability issues present in traditional methods.
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Enhanced Performance and Stability: This new controller, demonstrated through simulations and real-world experiments, significantly outperforms conventional PID controllers by offering superior stability, chattering reduction, and adaptability to varying environments and external disturbances.
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Broad Application Potential: The technology enables ballbots to be effectively utilized in various sectors, including assistive robotics for mobility-challenged individuals, service robotics in dynamic settings like hospitals and airports, and autonomous delivery systems that maintain stability under unpredictable conditions.
- Sustainable Robotics and Industry Impact: By optimizing energy consumption and improving reliability, the NPID-RBFNN controller paves the way for broader adoption in industries such as logistics, healthcare, and retail, promoting efficient and safe operations while reducing human workload.
New Controller Enhances Ballbot Technology
A team of researchers from multiple universities has developed a revolutionary controller for ballbots. Ballbots are unique robots that can move in all directions. However, controlling them poses several challenges, especially when it comes to maintaining balance in unpredictable environments.
Traditional controllers, like proportional integral derivative (PID), struggle with these demands. Other methods, such as sliding mode control, can lead to issues like chattering. Therefore, researchers sought a solution that combines the reliability of PID with the adaptive learning capabilities of neural networks.
Dr. Van-Truong Nguyen from Hanoi University of Industry leads the research team. They published their findings in the journal Engineering Science and Technology on January 1, 2025. The study features collaboration from academics across Vietnam, Japan, the United Kingdom, Taiwan, and India.
The innovative controller they designed, called the Nonlinear PID (NPID) controller, works alongside a radial basis function neural network (RBFNN). This combination enables lightweight calculations and improved stability. Moreover, it effectively reduces chattering and enhances the robot’s resistance to external disturbances.
Through careful optimization and continuous adjustments, the controller adapts during operation to maintain balance. Researchers emphasized system stability using Lyapunov theory. Their simulations and real-world tests show that the NPID-RBFNN controller outperforms traditional methods in various settings.
Dr. Nguyen envisions a wide range of applications for these advanced ballbots. He mentions their potential in assistive robotics, where they can aid individuals with mobility challenges in complex settings. Additionally, ballbots could serve in dynamic environments like restaurants, hospitals, and airports.
The team’s work addresses key issues in controlling robots within nonlinear and dynamic scenarios. By minimizing erratic movements, the new controller enhances energy efficiency, promoting sustainable robotics. This improvement boosts the overall reliability of ballbots in public and private spaces, making them safer for users.
Dr. Nguyen believes industries such as logistics, healthcare, and retail could greatly benefit from these advancements. He asserts that robots equipped with this technology could improve service quality while reducing human labor. As research continues, the potential for robots in everyday applications looks promising.
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