The optimizing space trajectory by using the inverse kinematics, direct dynamics and intelligent damper controlling with proper neural network

One of the more important in the field of robotics is the control and optimizing of the space trajectory of the tool center point (TCP). The paper present one new method of the controlling the direct and inverse kinematics, the direct and inverse dynamics and the Fourier spectrum. The control was designed by using proper neural network established after modeling and simulation the dynamic behavior. Finding the better solution of the neural network designed to solve the inverse kinematics and the direct dynamics problems and to obtain quickly the convergence process of the Fourier target spectrum with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The paper shown the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network Bipolar Sigmoid Hyperbolic Tangent with Time Delay and Recurrent Links BSHTNN(TDRL) type. All obtained results were been verified by applying the proper direct kinematics and inverse dynamics virtual LabVIEW instrumentation. Finally we were obtained one optimal neural network and one complex controller what can optimize the kinematics, dynamics and vibrations and obtain one maximum trajectory errors before 2%. The precision of the space orientation position was increased by using in the proper controlling schema, the intelligent magnetorheological damper (MRD) and the same neural network type, researched in this paper. Finally was obtained the decrease of the TCP errors before 1%. The proper neural network, the controller design, the results and the virtual LabVIEW instrumentation could be used in many other researches in this field.