工程科学与技术   2019, Vol. 51 Issue (4): 157-162

Research on PID Position Control of Multi-joint Manipulator for Rock Drill Car
LUO Hongbo, SHI Xianyang, WEI Peng, WANG Hao
School of Mechanical Eng., Sichuan Univ., Chengdu 610065, China
Abstract: Hydraulic drilling rigs play an important role in modern tunneling construction. Existing hydraulic drilling rigs need to control 6 hydraulic cylinders and 2 hydraulic motors separately when searching for holes. The electro-hydraulic proportional valve controls the position control system of the hydraulic cylinder. Taking the position control of the arm cylinder by the electro-hydraulic proportional valve as an example, the other joint control systems can be deduced by analogy. The position control system of the hydraulic cylinder controlled by the electro-hydraulic proportional valve is actually a system that corrects the displacement of the hydraulic cylinder in real time during the drilling operation. This system can control the deviation between the desired displacement and the actual displacement of the hydraulic cylinder, which requires adding a controller to the control section. The structure of the fuzzy control system was designed, and the method and process of Fuzzy PID parameter tuning were discussed. Then the fuzzy controller of the electro-hydraulic proportional valve control hydraulic cylinder was designed and simulated by MATLAB’s Fuzzy toolbox and SIMULINK module. The simulation results showed that in this valve-controlled hydraulic cylinder control system, the Fuzzy PID controller has strong anti-interference ability, fast response and stable performance.
Key words: hydraulic drilling jumbo    electro-hydraulic proportional valve    controller    Fuzzy PID

1 模糊PID控制器的设计 1.1 阀控液压缸模糊PID控制器的构成

 图1 模糊PID控制系统框图 Fig. 1 Fuzzy PID control system block diagram

1.2 模糊子集以及隶属度函数的确定

1.3 变量的论域、量化因子和比例因子 1.3.1 论域的确定

1.3.2 量化因子的确定

 ${K_{\rm L}} = \frac{X}{x}$ (1)

 X = \left\{\begin{aligned} & X \text{，}{K_{\rm L}}x \ge X\text{；} \\ & {{K_{\rm L}}x}\text{，}{K_{\rm L}}x < X\text{；} \\ & { - X}\text{，}{K_{\rm L}}x \ge - X \end{aligned}\right. (2)

1.3.3 比例因子的确定

 ${K_{\rm b}} = \frac{y}{Y}$ (3)

2 模糊PID的设置及仿真 2.1 模糊PID的设置

 图2 模糊系统控制图 Fig. 2 Fuzzy system control chart

 图3 控制器参数选择 Fig. 3 Controller parameter selection

 图4 e隶属度曲线 Fig. 4 e membership curve

 图5 ec隶属度曲线 Fig. 5 ec membership curve

 图6 Kp隶属度曲线 Fig. 6 Kp membership curve

 图7 Ki隶属度曲线 Fig. 7 Ki membership

 图8 Kd隶属度曲线 Fig. 8 Kd membership curve

 图9 模糊控制规则编辑 Fig. 9 Fuzzy control rule editing

 图10 模糊规则控制曲面观测 Fig. 10 Fuzzy rule control surface observation

 图11 模糊规则观察窗 Fig. 11 Fuzzy rule observation window

2.2 模糊PID的仿真

 图12 模糊PID仿真模型 Fig. 12 Fuzzy PID simulation model

3 控制系统仿真结果分析

 图13 PID和模糊PID的液压缸位移阶跃响应 Fig. 13 Hydraulic cylinder displacement step response of PID and Fuzzy PID

 图14 外载荷为10 000 N时PID和模糊PID的液压缸位移阶跃响应 Fig. 14 Hydraulic cylinder displacement step response of PID and Fuzzy PID with external load of 10 000 N

4 结　论

1) 在阀控液压缸的系统上建立了模糊PID的控制模型，通过给定阶跃信号和外载荷来分析该模糊控制系统的优劣，并与PID控制系统进行对比，得出模糊PID无论是从稳定性还是动态特性都要优于PID，符合设计要求。

2) PID与模糊PID在控制系统中的调节作用和整个控制系统的设计过程息息相关，特别是在进行模糊PID的设计时，模糊控制器的设计时基于人的经验的，因此论域、量化因子和比例因子的选取都会对结果产生较大的影响，以至于不能得到较为理想的控制曲线。可将模糊逻辑与神经网络相结合起来，这样得到一个优化后的参数，取得最佳控制效果。

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