# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
## 1 Introduction  
Rock mass control in coal mine roadways is pivotal for underground safety. Nevertheless, automation in roadway excavation and support still lags behind, leading to a pronounced imbalance between mining and bolting operations [1]. Conventional bolt support workflows are multi-step, time-consuming and labor-intensive, which heightens the risk of accidents [2, 3]. Although purpose-built equipment--such as bolter miners and drill-bolter integrated rigs--has been introduced, the installation of drill rods and anchoring agents continues to rely on manual intervention [4, 5]. In line with the move toward unmanned operations, the hydraulic control system of an anchor drilling rig must deliver high positioning accuracy, fast dynamic response and strong robustness under harsh, variable conditions; traditional PID controllers struggle to satisfy all of these requirements simultaneously. To address this, researchers have conducted extensive research on the automation of anchor drilling rigs and their control performance. The Swedish Sandvik modular drilling rig is equipped with a BH bolting head and an RD rock drill, enabling automated installation of 1.5-3.0 m rock bolts [6]. The Komatsu ZB21 bolter, produced in Japan, integrates a Jennmar pumpable resin system that injects anchoring resin into the drilled hole after boring, thereby improving ground support efficiency [7]. The [PERSON] drill rig from the United States features built-in dust removal ports coupled with a fan synchronization system, effectively reducing concentrations of dust generated during drilling. The integrated drilling-grouting-bolting rig developed by China Coal Technology's Taiyuan Institute incorporates a grout mixer within the anchor bolt, enabling mechanized support and improving efficiency in inclined roadways [8]. Reference [9] proposed a bolt-grouting reinforcement method for deep fractured rock masses, combining prestressed anchors with self-stress grouting to address grout-rock interface relaxation caused by cement-based material shrinkage. Although these advancements have significantly enhanced the automation level of bolting operations, most systems still require manual assistance for critical tasks such as anchoring agent installation. This residual dependence on human intervention limits their suitability for fully unmanned deployment. Furthermore, achieving high-precision hydraulic control is an essential prerequisite for autonomous bolting, particularly in response to complex underground conditions and dynamic disturbances. Existing studies on anchor drilling rig control have explored various approaches. Reference [10] developed a straddle-type automatic anchoring rig, enabling synchronous parallel excavation-support operations and mitigating coupling vibrations between surrounding rock and the anchoring system to enhance drilling stability. Reference [11] developed an adaptive control approach with genetic algorithm-optimized PID parameters to dynamically adjust rotation speed and propulsive force, thereby mitigating drill pipe damage and enhancing coal cutting efficiency. Reference [12] studies spool displacement characteristics within a load port independent control system and designs a two-level fuzzy PID controller along with its related algorithms, thereby achieving favorable outcomes in pressure-flow composite control. However, existing control research for anchor drilling rigs struggles to simultaneously achieve both high precision and strong robustness under complex working conditions. In contrast, fractional-order control (FOC) theory, proposed by [PERSON] [13], has demonstrated potential advantages in dynamic response and robustness, and has progressively achieved favorable application results in various fields such as precision machining control, wind turbines, electrohydraulic systems, twin rotor systems, rail vehicle tilt control, and industrial electrical drives [14; 15; 16; 17].