Research On High-accuracy Automatic Attitude Control Of Slurry Pressure Balance Shield Machine

Slurry pressure balance shield(SPB)is a large-scale tunnel excavation equipment that integrates excavation,grouting,slag discharge,lining and measurement.SPB is suitable for soft sandy soil layer so that it can realize long-distance complicated geological construction.SPB is mainly composed of cutter system,mud circulation and separation processing system,propulsion system,in which the latter is the key subsystem that guarantees SPB tunnels along the predetermined axis and reduces error.At present,the research on the shield machine is mainly focused on the earth press balance shield(EPB).Also,the attitude control is focused on trajectory planning and modeling,which is difficult to apply in actual tunneling because of ignoring external load.Moreover,the attitude adjustment in domestic engineering relies on manual adjustment.Compared with the status quo that developed countries have employed fuzzy control theory to gradually realize automatic attitude adjustment,intelligent and high-precision attitude adjustment technology of SPB is particularly prominent.A test platform of SPB with a diameter of 2.5m is designed with the fund of a major science and technology project of Henan province.The historical tunneling data is used for regression learning.An intelligent decision system of attitude adjustment is designed.A model referenced adaptive control strategy is proposed.The result of simulation test verifies the effectiveness of this system.The main work of this thesis is as follows:1.The load and propulsion of SPB in the actual tunneling was analyzed,and the Lagrangian dynamic model of SPB in tunneling process is set up;the principle of the propulsion hydraulic system is analyzed,and mathematical model of electro-hydraulic proportional 3-way pressure reducing valve that controls single-acting hydraulic cylinder is established;2.The distribution of data in different attitude adjustment situations is described by analyzing the manual operation-based attitude adjustment technology.A principle of data cleaning to select excellent attitude adjustment is proposed.The kernel K means clustering method is used to Cluster and clean sample data;3.The least-squares learning method with loss is introduced and by combining with the loss of first-order and second-order norm,a robust learning method based on Huber loss minimization is designed.By using Gaussian kernel function and 8-fold cross-validation,sample data the horizontal and vertical attitude adjustment is training,and the R-squared score of learning models are about 76.5%and 71.5%,respectively.An attitude adjustment model is established by selecting learning parameters with largest R-squared score;4.Through establishment of Diophantine equation,parameter tuning law and control law of model referenced adaptive controller of propulsion system is determined.Controlling processes with load disturbance and without load disturbance are respectively simulated and analyzed.The results showed that the adaptive controller is sensitive to load disturbance and has a certain degree of robustness;5.Through integrating robust learning model,adaptive controller,propulsion system model and dynamic model,a complete attitude adjustment intelligent decision system is established and simulated.The results show that the intelligent decision system have a good performance on attitude adjustment of horizontal direction,and there is a slight lag in attitude adjustments in the vertical for large weight and inertial of SPB;6.Aiming at the motion characteristics of SPB in real tunneling,a 5-DOF attitude simulation test platform is designed.The attitude solution equation is established through the propulsion system with Hooke hinge and the follow-up supporting mechanism.The Supporting hydraulic system is designed and simulated via a fuzzy PID controller.The simulation results show that the support cylinder has a good following function and can dynamically compensate the weight of shield's body to achieve attitude simulation with 5-DOF.