Search

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer (LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8%. This greatly improves the stability and security, which is exactly what the drilling process requires. [ABSTRACT FROM AUTHOR]

Normal and stable operations of hydraulic systems are of great importance to the safety and efficiency of industrial production processes. Accurate and prompt diagnosis of fault types and degrees can ensure hydraulic systems return to normal in the early stage of faults and thus can prevent serious accidents. However, the structure of hydraulic systems is complex, and some faults may occur simultaneously. In addition, many hydraulic systems have multi-rate data collected from different sensors. Such problems cause great challenges to fault diagnosis in hydraulic systems. Motivated by the above issues, this paper proposes a deep learning method to diagnose faults and identify fault degrees in hydraulic systems using fully convolutional networks (FCNs) and deep feature fusion. The main contributions are twofold: (1) A new fault diagnosis framework is designed to identify both the fault types and degrees in the presence of multiple faults; and (2) deep feature extractors composed of multiple superimposed convolutional blocks are designed to extract deep features from multi-rate time series, and such features are then fused via flattening and concatenating and fed into the fault diagnosis model. Case studies based on a hydraulic system test bed are provided to demonstrate the effectiveness and superiority of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and challenging. In view of these problems, we propose a population randomization-based multi-objective genetic algorithm. The gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time. [ABSTRACT FROM AUTHOR]