Papers by Author: Dan Paune

Abstract: The actual required productivity, accuracy and reliability impose that the robots must be optimized concerning the dynamic behavior. The joints and robots bodies are necessary to be optimized for their usability performance to assure the productivity requirements. The global dynamic compliance (GDC) is one of the most important dynamic parameters of the dynamic behavior of the industrial robots. The viscose global dynamic damper coefficient (VGDDC) is other important parameter of the dynamic behavior what must be optimized to obtain the desired dynamic behavior, the avoiding of the resonance frequencies. The paper shows one new assisted method of the GDC analyzes of the industrial robot with LabVIEW virtual instrumentation (VI) in three different cases: with/without smart magnetorheological damper (MRD) and with aero damper. The created VI-s assures to obtain the assisted research of the dynamic behavior. With this research was possible to determine in the frequencies domain, the robot GDC and the viscose global dynamic damper equivalent coefficient (VGDDEC) value in a case with MRD and finally the transmission of the vibration from the floor to the robot’s tool center point (TCP). This method and the created virtual LabVIEW instrumentation are generally and they are possible to apply in many others dynamic behavior researches.

Abstract: The paper showed the assisted research of one new model of digital dynamic neural network by using the LabVIEW proper virtual instrumentation and proper mathematical model. In the research were used some different way to optimize the convergence process, for example: using one time- delay of the first and second output from the neural layers; using the recursive link and time- delay; using the bipolar sigmoid hyperbolic tangent sensitive function replacing the sigmoid simple sensitive function. By on-line simulation of the neural network it is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and time- delay, to the gradient errors, in a convergence process. By on-line using the proper virtual LabVIEW instrumentation, were established some influences of the network parameters: number of input vector data, number of neurons in each layers, to the number of iterations before canceled the mean square error to the target. In the optimization research we used the minimization of the gradient error function between the output and the target.

Abstract: In the optimization of the trajectory or of the guidance of mobile robots one of the more important things is to assure one small difference between the output data of the system and the target. This paper show how on-line will be possible to establish one convergence way to the target without any influences of the input data or initial conditions of the weights or biases. The paper show the general components and the mathematical model of some more important neurons and one numerical simulation of the linear neural network. In the paper was used the least mean square (LMS) error algorithm for adjusting the weights and biases and incremental training by different training rate, finally to obtain one minimum error to the target.

Abstract: The paper shown one assisted method to construct simple and complex neural network and to simulate on-line them. By on-line simulation of some more important neural simple and complex network is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and delay of time. There are shown some important neurons type, transfer functions, weights and biases of neurons, and some complex layers with different type of neurons. By using the proper virtual LabVIEW instrumentation in on-line using, were established some influences of the network parameters to the number of iterations before canceled the mean square error to the target. Numerical simulation used the proper teaching law and proper virtual instrumentation. In the optimization step of the research on used the minimization of the error function between the output and the target.

Abstract: Finding the better solution of the neural network design to solve the inverse kinematics problem with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem. We were been studied the number of neurons in each layers, the sensitive function for the first and second layer, the magnifier coefficient of the trajectory error, the variable step of the time delay and the position of this block, the different cases of target data and the case when the hidden target data were adjusted. All obtained results were been verified by applying the proper direct kinematics virtual LabVIEW instrumentation. Finally we were obtained one optimal Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN(TDRL)) type, what can be used to solve the inverse kinematics problem with maximum 4% of trajectory errors.