Modeling And Control Research On Robotic Machining Process

Robotic machining has attracted attentions of scholars recently.In order to achieve efficient and high precision robotic machining,many scholars have paied their great effort on robotic machining research.As insufficient stiffness of robot,compared with CNC(computer numerical control)machine,deformation and vibration occur when machining force applies on robot end-effector,which results in a decrease of machining quality.In view of this,studies of trajectory planning and machining force control are conducted to increase robotic machining quality.This research aims to construct robotic machining model and reduce robotic machining deformation and vibration with the use of trajectory planning method and machining force control approaches.In order to achieve this target,robotic machining model including robot stiffness model,robotic machining dynamic model,and trajectory planning model is studied in this research.On the basis of robotic machining model,force controller are developed to control robotic machining force.Since the insufficient stiffness is the main cause of robotic machining vibration and deformation,the stiffness behaviour of robots with different kind of transmission system are first explored and analyzed.Based on the analysis result,robotic cutting dynamic model is constructed which contains of robotic stiffness model and traditional cutting model.The trajectory planning methods and force control approaches are then proposed to compensate for the error caused by robot deformation.The main contribution of this research are:The robotic stiffness model is constructed to explore robot stiffness behaviour.As the robot stiffness is impact by stiffness of transmission system,the stiffness formulas of transmission system key components are established to construct transmission system stiffness model.On the basis of this,the robotic stiffness model is then deduced according to the mapping model between transmission system stiffness and robot stiffness.The 3-DOF Cartesian robot and 6-DOF rotary joint robot are taken as examples to explain the process of stiffness calculation.Two stiffness simulations are conducted subsequently to validate the stiffness model and observe the robot stiffness behaviour in Cartesian space.In order to evaluate the influence of deformation and vibration caused by insufficient stiffness of robot,the robotic cutting dynamic model is studied.Due to insufficient stiffness,the cutting dynamic behaviour of robot machining is far more complicated compared with CNC machine and the traditional cutting model is not able to adequately illustrate the robotic cutting dynamics.In view of this,the generation and release process of deformation is analyzed and the relationship of robot stiffness,feed rate,machining angle and deformation is explored.Based on the analysis result,the dynamic model of robotic machining is established to illustrate robot machining behaviour.A deformation based trajectory planning method is proposed to compensate for machining error caused by robotic machining deformation.To construct a connection between deformation and trajectory planning,the relationship between feed rate and trajectory position and the relationship between feed rate and deformation are analyzed.B-spline curve is then taken as an example to illustrate the realization process of proposed trajectory planning optimization method and simulation is conducted to confirm the validity of proposed method.Apart from trajectory planning,machining force control is also able to compensate for machining error caused by deformation.Four machining force control approaches are proposed including fuzzy PID control approach,fuzzy sliding mode control approach,PD control approach based on PSO algorithm,and PD control approach based on adaptive sliding mode iteration algorithm.Considering the characteristics of nonlinear,fuzzy PID control approach is proposed to achieve subdivision control according to machining state.Considering the system disturbance caused by asymmetry of workpiece surface,system resonance,and other system noise,sliding mode is used to increase system stability and fuzzy sliding mode control approach is developed.As fuzzy rules is constructed based on expert knowledge and experience,control approach developed on the basis of fuzzy rules may be not able to meet the control requirement raised by unknown machining state.In view of this,a PD control model based on PSO algorithm is proposed.The advantage of PSO is that it can optimize control parameters according to machining state error.The adaptive sliding mode iteration algorithm is then used to increase the optimization speed of control parameters.In order to validate the effectivity of proposed dynamics model and control approaches,two experiment platforms are constructed,including 3-DOF Cartesian machining robot and 6-DOF rotary joint machining robot.Machining experiments are conducted with the use of this two robots,including robot stiffness idenification experiment,basic experiment of robotic machining,trajectory planning experiment,and machining force control experiments.