In a groundbreaking study, researchers have embarked on the development of a generalized linear-quadratic neurocontroller for crane-load systems, a significant advancement that could reshape operational efficiency in the mining sector. Led by Юрій Ромасевич from the Національний університет біоресурсів і природокористування України, this research aims to optimize the control of cranes, which are crucial for material handling in mining and construction.
The study, published in ‘Гірничі, будівельні, дорожні та меліоративні машини’ (Mining, Construction, Road and Melioration Machines), introduces a mathematical model where the control function is treated as the rate of change of driving force. This innovative approach increases the system’s order, allowing for a more refined control mechanism. “By reformulating the control problem, we can achieve smoother movements and minimize initial driving forces, which is essential for reducing mechanical stress on crane structures,” Rомасевич explained.
The research highlights the dual nature of the findings: while there are clear advantages such as enhanced smoothness in system operation and reduced dynamic load on the crane’s drive mechanism, there are also challenges. One notable drawback is the rapid increase in driving force at the start of movement, which could complicate practical implementation. This insight is particularly relevant for mining operations where heavy loads and precise movements are a daily requirement.
To further enhance the control system, the team employed a recursive solution to the Riccati equation, generating data arrays for training and testing an artificial neural network. This neural network serves as a universal approximator for the Riccati solutions, potentially revolutionizing how crane operations are optimized. “The ability to train a neural network on this data opens new avenues for real-time control adjustments, which is invaluable in dynamic environments like mining,” Rомасевич noted.
The research also delves into the parameters of the regulator, with optimal values determined for varying load weights between 60 and 25,000 kg and flexible suspension lengths ranging from 1.2 to 12 meters. These findings could lead to tailored solutions for different operational scenarios in the mining industry, enhancing productivity and safety.
As the mining sector continues to embrace automation and advanced technologies, this research stands out as a pivotal step towards smarter, more efficient operations. The implications for cost reduction and improved safety protocols are significant, making it a topic of keen interest for industry stakeholders. The integration of artificial intelligence with traditional engineering practices could very well define the future of crane operations in mining and beyond.
