采煤机和刮板输送机是综采设备群中的主要机电设备，二者协同完成煤壁的截割以及煤料的运输，其正常运转是煤炭持续生产的必要条件。以某矿综采工作面为研究对象，针对采煤机和刮板输送机运行速度不匹配，刮板输送机连续高速运转造成的能源浪费和效率低的问题，对采煤机和刮板输送机的协同控制问题进行研究，主要研究内容如下：

（1）根据采煤机和刮板输送机基本结构以及配套工作过程，对采煤机和刮板输送机协同调速方案进行研究和设计，具体包括有采煤机与刮板输送机协同调速决策模型构建、刮板输送机负载预测以及采煤机与刮板输送机速度控制器设计。

（2）研究刮板输送机载煤量计算模型，以刮板输送机能耗为主要优化目标，以刮板输送机运输效率和运输速度波动性作为辅助优化目标，获得综合优化指标，采用海鸥优化算法（Seagull Optimization Algorithm，SOA）求解，构建采煤机与刮板输送机协同调速决策模型，规划各阶段采煤机牵引速度和刮板输送机运输速度。通过Matlab仿真分析，使用协同调速决策模型对原始速度方案进行优化，可降低刮板输送机能耗28.7%，提高效率6.8%。

（3）在刮板输送机负载与电机电流正相关映射的基础上，建立基于改进海鸥优化算法（Improved Seagull Optimization Algorithm，ISOA）—BP神经网络的刮板输送机负载预测模型。以刮板输送机负载电流作为研究对象，使用ISOA优化的BP神经网络对负载电流进行预测实验，验证预测模型的有效性。

（4）设计基于ISOA的模糊PID控制器对采煤机和刮板输送机进行速度调节，使用ISOA对模糊PID量化因子、比例因子以及初始比例、微分和积分系数进行寻优，避免传统方式调参时间的浪费以及参数设置不佳。使用Simulink进行控制仿真，同时与PID和模糊PID以及SOA优化的模糊PID进行比较，验证ISOA优化的模糊PID控制器使得调节时间更短，超调量更小，控制更加准确和高效。

Shearer and scraper conveyor are the main mechanical and electrical equipment in the fully mechanized mining equipment group. They cooperate to complete the cutting of coal wall and the transportation of coal materials. Their normal operation is the necessary condition for the continuous production of coal. Taking a fully mechanized coal mining face in a mine as the research object, aiming at the problems of energy waste and low efficiency caused by the mismatch between the running speed of shearer and scraper conveyor and the continuous high-speed operation of scraper conveyor, the collaborative control of shearer and scraper conveyor is studied. The main research contents are as follows:

(1) According to the basic structure and supporting working process of shearer and scraper conveyor, the collaborative speed regulation scheme of shearer and scraper conveyor is studied and designed, including the construction of collaborative speed regulation decision model of shearer and scraper conveyor, the load prediction of scraper conveyor and the design of speed controller of shearer and scraper conveyor.

(2) This paper studies the calculation model of airborne coal volume of scraper conveyor, takes the energy consumption of scraper conveyor as the main optimization goal, and takes the transportation efficiency and speed fluctuation of scraper conveyor as the auxiliary optimization goal, obtains the comprehensive optimization index, and uses Seagull Optimization Algorithm (SOA) to solve it, and constructs the decision-making model of coordinated speed regulation of shearer and scraper conveyor, Plan the haulage speed of shearer and the transportation speed of scraper conveyor at each stage. Through MATLAB simulation analysis, the original speed scheme is optimized by using the collaborative speed regulation decision model, which can reduce the energy consumption of the scraper conveyor by 28.7% and improve the efficiency by 6.8%.

(3) Based on the positive correlation mapping between scraper conveyor load and motor current, a scraper conveyor load prediction model based on Improved Seagull Optimization Algorithm (ISOA)-BP neural network is established. Taking the load current of scraper conveyor as the research object, the load current is predicted by using ISOA Optimized BP neural network, and the effectiveness of the prediction model is verified.

(4) The fuzzy PID controller based on ISOA is designed to adjust the speed of shearer and scraper conveyor. The quantitative factor, proportional factor, initial proportional, differential and integral coefficient of fuzzy PID are optimized by using ISOA, so as to avoid the waste of parameter adjustment time and poor parameter setting in the traditional way. Simulink is used for control simulation, and compared with PID, fuzzy PID and SOA optimized fuzzy PID, it is verified that the fuzzy PID controller optimized by ISOA makes the adjustment time shorter, the overshoot smaller, and the control more accurate and efficient.