Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

摘要

The dissertation proposes two control strategies, which include the trajectory planningand vibration suppression, for a kinematic redundant serial-parallel robot machine, withthe aim of attaining the satisfactory machining performance.For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a setof trajectories in the robot's joint space are generated based on the best stiffnessperformance of the robot along the prescribed trajectory.To construct the required system-wide analytical stiffness model for the serial-parallelrobot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation.The modified method is an evolution of Gosselin's lumped model that can account for thedeformations of a flexible link in more directions. The effectiveness of this VJM variantis validated by comparing the computed stiffness results of a flexible link with the thoseof a matrix structural analysis (MSA) method. The comparison shows that the numericalresults from both methods on an individual flexible beam are almost identical, which, insome sense, provides mutual validation. The most prominent advantage of the presentedVJM variant compared with the MSA method is that it can be applied in a flexiblestructure system with complicated kinematics formed in terms of flexible serial links andjoints. Moreover, by combining the VJM variant and the virtual work principle, a systemwideanalytical stiffness model can be easily obtained for mechanisms with both serialkinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of akinematic redundant serial-parallel robot machine is constructed based on integration ofthe VJM variant and the virtual work principle. Numerical results of its stiffnessperformance are reported.For a kinematic redundant robot, to generate a set of feasible joints' trajectories for aprescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribedlocation of the end-effector, the robot permits an infinite number of inverse solutions,which consequently yields infinite kinds of stiffness performance. Therefore, adifferential evolution (DE) algorithm in which the positions of redundant joints in thekinematics are taken as input variables was employed to search for the best stiffnessperformance of the robot. Numerical results of the generated joint trajectories are givenfor a kinematic redundant serial-parallel robot machine, IWR (IntersectorWelding/Cutting Robot), when a particular trajectory of its end-effector has beenprescribed. The numerical results show that the joint trajectories generated based on thestiffness optimization are feasible for realization in the control system since they areacceptably smooth. The results imply that the stiffness performance of the robot machinedeviates smoothly with respect to the kinematic configuration in the adjacent domain ofits best stiffness performance.To suppress the vibration of the robot machine due to varying cutting force during themachining process, this dissertation proposed a feedforward control strategy, which isconstructed based on the derived inverse dynamics model of target system. Theeffectiveness of applying such a feedforward control in the vibration suppression hasbeen validated in a parallel manipulator in the software environment. The experimentalstudy of such a feedforward control has also been included in the dissertation. Thedifficulties of modelling the actual system due to the unknown components in itsdynamics is noticed. As a solution, a back propagation (BP) neural network is proposedfor identification of the unknown components of the dynamics model of the target system.To train such a BP neural network, a modified Levenberg-Marquardt algorithm that canutilize an experimental input-output data set of the entire dynamic system is introduced inthe dissertation. Validation of the BP neural network and the modified Levenberg-Marquardt algorithm is done, respectively, by a sinusoidal output approximation, asecond order system parameters estimation, and a friction model estimation of a parallelmanipulator, which represent three different application aspects of this method.