In the heart of China’s coal mining industry, a revolutionary approach to hydraulic support systems is emerging, promising to enhance both safety and efficiency. Mingze Sun, a researcher at the School of Artificial Intelligence, China University of Mining Technology-Beijing, has developed a groundbreaking method for controlling hydraulic support robots, which could significantly impact the future of coal mining operations.
Traditional hydraulic support systems rely on centralized control, where a single controller manages the entire operation. This setup, while straightforward, has a critical flaw: if the central controller fails, the entire system can grind to a halt, posing significant safety risks and operational inefficiencies. Sun’s innovative solution addresses this vulnerability by introducing a decentralized, distributed cooperative control method. “By treating each hydraulic support as an intelligent individual with its own perception, decision-making, and control capabilities, we can create a more robust and adaptable system,” Sun explains.
The new approach involves designing a mathematical model for the single moving mechanism of hydraulic supports and developing an adaptive communication topology based on state information. This allows the system to dynamically adjust to various moving modes, ensuring consistent and reliable performance even under complex disturbances. Sun’s method also incorporates a distributed extended state observer and a disturbance feedback loop, which actively estimates and cancels out unknown disturbances, thereby enhancing the system’s robustness.
To validate the effectiveness of this new control method, Sun and his team conducted co-simulations using Amesim-Simulink. The results were compelling: the distributed cooperative control method demonstrated superior control accuracy and active anti-disturbance capabilities compared to traditional PID (Proportional-Integral-Derivative) centralized control methods. “Our simulations showed that the new method not only improves the efficiency and safety of moving supports but also offers high reliability, good consistency, and strong active anti-interference abilities,” Sun notes.
The implications of this research for the energy sector are profound. As coal mining operations strive to enhance safety and efficiency, the ability to automatically move and self-straighten hydraulic support groups under various complex disturbances could be a game-changer. This technology could lead to more stable and efficient coal mining faces, reducing the risk of accidents and improving overall productivity. The distributed nature of the control method also means that the system can adapt to different moving modes, making it highly versatile and scalable.
Sun’s work, published in ‘Meitan xuebao’ (translated to Coal Science and Technology), represents a significant step forward in the field of intelligent coal mining. As the industry continues to evolve, the integration of advanced control systems like Sun’s could pave the way for smarter, safer, and more efficient mining operations. The future of coal mining may well be shaped by such innovative technologies, driving the sector towards greater sustainability and productivity.