Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator

There are wide applications of the parallel manipulators in the industry field because of the high speed and accuracy, but difficulties are also unavoidable in the controlling process for the systematic time-varying and coupling characteristics. Therefore, the approaches for better control performance are vital to the application of parallel manipulator. In this paper, one newly invented 2-DOF (degree of freedom) parallel manipulator called Diamond 600 is investigated as an objective for Pick and Place operation. Firstly, the dynamic model of mechanism and servo driving subsystems are formulated using virtual work principle and field orient control principle, respectively. According to the equivalent torque principle, a mechatronic (motor-mechanism coupling) dynamic model is deduced for real-time control. Secondly, the neural network optimized computed torque control algorithm is described in detail for the proposed coupling model. At last, a series of numerical simulations and experiments are carried out, respectively, to prove the validity of the above methods, and results verify the favourable tracking ability and robustness.