The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψ_f) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

In order to solve the problems of nonlinearity, uncertainty and coupling of multi-hydraulic cylinder group platform of a digging-anchor-support robot, as well as the lack of synchronization control accuracy of hydraulic synchronous motors, an improved Automatic Disturbance Rejection Controller-Improved Particle Swarm Optimization (ADRC-IPSO) position synchronization control method is proposed. The mathematical model of a multi-hydraulic cylinder group platform of a digging-anchor-support robot is established, the compression factor is used to replace the inertia weight, and the traditional Particle Swarm Optimization (PSO) algorithm is improved by using the genetic algorithm theory to improve the optimization range and convergence rate of the algorithm, and the parameters of the Active Disturbance Rejection Controller (ADRC) were adjusted online. The simulation results verify the effectiveness of the improved ADRC-IPSO control method. The experimental results show that, compared with the traditional ADRC, ADRC-PSO and PID controller, the improved ADRC-IPSO has better position tracking performance and shorter adjusting time, and its step signal synchronization error is controlled within 5.0 mm, and the adjusting time is less than 2.55 s, indicating that the designed controller has better synchronization control effect.

Aiming at the real-time perception problem of key target objects caused by harsh environmental factors of high dust, low illumination, motion blur, and multi-target mixing in the comprehensive excavation working face of coal mine, a multi-target detection and tracking algorithm based on DDEB-YOLOv5s and StrongSORT is proposed. First, the YOLOv5s model is improved by using C3-Dense module, decoupled head, ECIoU loss function, and weighted bi-directional feature pyramid network to enhance the detection performance of the model in complex backgrounds of coal mine and complete the design of the DDEB-YOLOv5s multi-target detection network. Second, the DDEB-YOLOv5s algorithm is used as a detector and combined with the StrongSORT tracking algorithm to track critical equipment and miners in the complex context of coal mine. Experiments were conducted on the dataset of comprehensive excavation working face, and the experimental results show that the proposed DDEB-YOLOv5s has the best integrated detection performance compared with other YOLO series target detection algorithms, and its mean value of detection accuracy reaches 91.7%, which is 4.9% higher than that of the original YOLOv5s model. In addition, compared to the three tracking models, (YOLOv7-tiny)-(BoT-SORT), YOLOv5s-DeepSORT, and YOLOv8s-Bytetrack, the (DDEB-YOLOv5s)-StrongSORT model has the best tracking performance (with a mean tracking accuracy of 94.2%) and the least number of identifier switches. Finally, the real-time perception method proposed in this study for the key target of the coal mine working face can provide new technical support and effective guarantee for coal mine safety production.

To improve the roadway adaptability and control accuracy of anchoring equipment, a stepping anchoring device was designed. A permanent-magnet synchronous motor control and a harmonic suppression algorithm were integrated to optimize the dynamic control system of stepping-type anchoring equipment. The results of an experimental simulation and analysis showed that when the coefficient of coal rock hardness f = 5, 6, and 7, the pulsation coefficient of the hydraulic pump outlet pressure, hydraulic motor output speed, and pump-controlled hydraulic cylinder advance speed in the hydraulic circuit of a pump-controlled motor did not exceed 3% after the equipment based on sliding mode control (SMC) entered the steady state, while the maximum pulsation coefficient was only 32.5% of the PI control. Based on the SMC, the harmonic components of the permanent magnet synchronous motor in the power system were suppressed and compensated for. This enhanced the stiffness of the hydraulic system under motor drive. When the rock stiffness factor gradually changed from f = 5 to f = 8 and increased suddenly from f = 5 to f = 6, the pressure overshoot at the outlet of the hydraulic pump of the pump-controlled motor system was reduced from 11.19% to 7.97% and from 61.19% to 52.88%, respectively, compared with that before the optimization. It was thereby proven that SMC based on harmonic suppression can effectively reduce the system pulsation caused by the multi-factor coupling of anchoring equipment and provide technical support for the optimal control of the power system of stepping-type anchoring equipment.

In view of the risk of collision with humans or equipment arising from a lack of protection in the operation process of combined support and anchor equipment on the heading face, this paper designs a safety interlock system for combined support and anchor equipment. Firstly, a mathematical model of hydraulic power system control and a valve control system based on feedforward–feedback optimization were established according to the power demand of the combined support and anchor equipment. Secondly, according to the reliability indexes of the safety interlock system, corresponding sensor, logic control and execution modules were designed. Ultrasonic sensor groups were arranged at the key positions of the combined support and anchor equipment to capture the position information in real time when the equipment was moving. Thus, the pump-valve hydraulic system was controlled through closed-loop feedback. The experimental results show that the safety interlock system of the combined support and anchor equipment can adjust the revolving speed of the permanent magnet synchronous motor (PMSM) in real time according to the distance from the obstacle, so as to control the pump outlet flow, and then perform interlocking safety control of the hydraulic cylinder’s movement speed. The system can effectively prevent damage to the surrounding equipment or personnel arising from equipment malfunction.

To address the problem of rock hardness recognition in comprehensive excavation-face anchorage operations, a rock hardness recognition method based on feature optimization and an improved sine cosine algorithm optimised decision tree (CART) is proposed. First, the signal was reconstructed by successive variational mode decomposition (SVMD) and the waveform factor method. Second, the time–frequency matrix of the signal was calculated using short-time Fourier transform (STFT), S transform (ST), and frequency-corrected generalised S-transform (FCGST), and the dimension was reduced employing singular value decomposition (SVD). Finally, based on the improved sine cosine optimization algorithm (ISCA), the minimum leaf node size and maximum number of splits in the decision tree were optimised, and different rock hardness recognition models were established. Based on a self-developed experimental platform for the integration of excavation, support, and anchor, multiple lithologic combinations were determined. The results of drilling experiments show that by using the recognition model with SVMD, waveform factorisation method reconstruction, FCGST feature extraction, and ISCA key parameter optimization, the accuracy of rock hardness recognition was improved by 9% and 4%, respectively, and the recognition efficiency reached 97.33% compared with STFT and ST feature extraction. The effectiveness of the rock hardness identification method for drilling of excavation support and anchor equipment based on feature preference and decision tree was further verified. This study can provide technical support for the identification of rock hardness under complex working conditions of a comprehensive excavation face.