# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
###### Abstract  
Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit-Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors. trajectory tracking; sliding mode control; particle swarm optimization; modified D-H +
Footnote †: journal: Applied Sciences  
1  
Quality Development Guidelines for the Coal Industry\", issued in 2021, clearly proposed to significantly reduce the number of underground operators. China University of Mining and Technology and Harbin Institute of Technology jointly developed a drill robot that can automatically transport, install, and dismantle drilling bars. Chongqing Research Institute achieved fully automatic drilling of a drill \(Z\) through research and testing in the areas of automatic installation and dismantling drilling bars and automated drilling. In the intelligent operation of mechanical water detection, how to ensure the connection accuracy of drilling bars when automatically completing the assembly tasks is the core technical issue for the development of water detection machinery [2]. The study of control algorithms for supplied material devices to optimize the trajectory tracing controller is a concrete safeguard measure to improve the connection accuracy [3,4]. Based on the theory of inverse kinematics, the application of the trajectory tracking control algorithm converts the Cartesian trajectory tracking control problem to a joint trajectory tracking control problem to study the accuracy of the control methods of the supplied material device [5]. Interference factors in the underground environment are numerous and uncertain, such as external disturbances of the controlled system, limiting the increase in tracking control accuracy [6,7]. At the same time, the supplied material device, as a nonlinear strong coupling system [8], has higher requirements for the control algorithm. The sliding mold control is essentially a special type of nonlinear control, in the design of which the sliding modules are unrelated to the parameters and disturbances of the controlled object, which can lead to effects of uncertainty and nonlinearity in most practical systems [9,10]. The good characteristics of the dynamic response make this control algorithm widely used in nonlinear control systems [11]. Therefore, this paper improves the control of the supplied material device by improving the sliding mold controller.