Research On The Intelligent Optimal Control Of The Beam Oil Pumping Unit

The object of study of this thesis is one kind of ground equipment for extracting oil,which is used widely in current.The beam pumping oil unit consists of electric motor,reducer,crank,connecting rod,beam,horse head,which are part of ground,and sucker rod,pump,which are part of underground.The horse head is driven by reducer,four connecting rod,which are driven by motor to move up and down and iteratively to pump the oil,.Because of the high energy consumption caused by beam pumping oil unit,so control of energy saving is the one of important problem to be solved.Drawbacks and restrictions exist in common traditinal energy saving methods to complex working environment and conditions of beam pumping oil unit.The structure of beam pumping oil unit is complex to built model,so its body is simplified as four connecting rod.First,the thesis establishes the kinematic equation and dynamic equation of the beam pumping oil unit by refer to the inference rules of four connecting rod model,which is given the mathematical model of beam pumping oil unit and simulation analysis is conducted.Second,in order to reduce the starting torque and starting current of pumping unit and make the alternating load is more matched with the motor,vector control technology is adopted by source of energy consumption–asynchronous motor in this thesis for improving the efficiency of pumping unit system.Asynchronous motor is a kind of complex,nonlinear mimo system with strong coupling.It is difficult to control invalid with traditional methods which are based on model.With intelligent control methods develop rapidly,effective control is achieved by fuzzy logic for intelligent control of complex mechanical system.For the crank fluctuation caused by the motor speed causing the instability of system,base on the model of Asynchronous motor,adaptive fuzzy PI controller was designed and system simulation study was conducted in this dissertation.And the results of simulation indicated dynamic performance of system has been improved,thus proving the validity of the controller.Because of the simple fuzzy control have its disadvantages in parameters adjustment,so the particle swarm algorithm is applied to optimize adaptive fuzzy controller.The controller has the advantages of multi objective optimization on the basis of summing up information,and the simulation results verify the good robustness and adaptability of the intelligent controller.In this thesis,genetic algorithm and particle swarm algorithm are combined to optimize the parameters of adaptive fuzzy controller to find the optimum solution,for particle swarm is easy to find the local optimal value instead of global optimal value.The simulation results indicate that controller optimized by particle swarm algorithm is more valid than adaptive fuzzy controller,the controller optimized by combination of particle swarm algorithm and genetic algorithm is more valid and convergence fast than controller optimized by particle swarm algorithm.The thesis makes a research of the simulation for intelligent control system,and the simulation results verify the validity of it.Finally,combining with the research of the thesis,the model is built by TI C2000 toolbox of MATLAB/SIMULINK,set the parameters of DSP hardware and connecting the DSP28335 target board to generate executable C code automatically in the CCS,The operation results show the correctness of the system.