Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators.

With the development of the mining industry, safety issues in underground operations are becoming increasingly relevant. Complex gas conditions in mines, including the presence of explosive and toxic gases, pose a serious threat to the lives of miners and the stability of production processes. This paper describes the development and modeling of an integrated fire monitoring and automatic extinguishing system that combines gas collection, concentration analysis, and rapid response to emergencies. The main components of the system include the following: a gas collection module that uses an array of highly sensitive sensors to continuously measure the concentrations of methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S) with an accuracy of up to 95%; a gas analysis module that uses data processing algorithms to identify gas concentration threshold exceedances (e.g., CH₄ > 5% vol. or CO > 20 ppm); and an automatic fire extinguishing module that activates nitrogen supply, ventilation, and aerosol/powder fire extinguishers when a threat is detected. Simulation results in MATLAB/Simulink showed that the system reduces the concentration of hazardous gases by 30% within the first 2 s after activation, which significantly increases safety. Additionally, scenarios with various types of fires were analyzed, confirming the effectiveness of the extinguishing modules in mines up to 500 m deep. The integrated system achieves 95% gas detection accuracy, 90 ms response latency, and 40% hazard reduction within 3 s of activation, verified in 500 m deep mine simulations. Quantitative comparison shows a 75% faster response time and 10% higher detection accuracy than conventional systems. The proposed system demonstrates high reliability in difficult conditions, reducing the risk of fires by 75% compared to traditional methods. This work opens up prospects for practical application in the coal industry, especially in regions with a high risk of spontaneous coal combustion, such as India and Germany.

The paper presents the results of the development of a single integrated solution for the automation of technological processes of service enterprises of the oil and gas sector, which allows to organize the work of production and management in a single information space. Algorithms have been developed to solve individual problems that are interconnected into certain algorithmic modules. The results of the integration analysis of modern automation systems of the ERP-class are presented, in particular, the level of adaptation to the stages of the product life cycle, compliance with standards and safety requirements, the complexity of the transition to the automation system. The results of the analysis of possible risks of the project during the implementation of this algorithm are presented. The practical significance of the development of a single production management algorithm is described. Finally, the treatise evaluates the main advantages and economic effect of the introduction of an automation system for an enterprise engaged in the production of oil and gas pipes at the process control level.

Equipment selection is a critical decision in mining operations, directly influencing production efficiency, maintenance requirements, and operational costs. However, this decision is complicated by significant uncertainty surrounding equipment performance and remaining service life. This paper presents a hybrid decision support framework that integrates Fuzzy Logic, Pareto Optimality, and a Genetic Algorithm (GA) to address the challenge of roadheader selection under such uncertainty. The proposed Fuzzy–Pareto–GA approach applies fuzzy logic to model the inherent uncertainty in performance data; employs Pareto optimization to identify optimal trade-offs between multiple, often conflicting criteria; and utilizes a genetic algorithm to efficiently navigate the solution space. The framework is validated using real-world data from an operating mining company, considering three key criteria: operating time, remaining service life, and the remaining service life ratio. The results demonstrate that the fuzzy–Pareto approach effectively identifies a set of non-dominated solutions, and the robustness of these rankings is confirmed through a comprehensive sensitivity analysis. The proposed framework offers mining engineers a transparent and uncertainty-aware tool for equipment selection, a decision that serves as a critical foundation for effective production process optimization.