A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

Grouping strategies for MPS soot transport model and its application in large-scale enclosure fires

A soot transport model called Multi-Particle-Size model (MPS model) was developed to improve the prediction of soot movement by considering the uneven mass size distribution of soot particles and the influence of particle size on the gravitational settling. The model requires a sophisticated grouping strategy to divide the soot particles into several groups and determine the representative size for each group. In this paper, several soot particle grouping strategies and the method to calculate the representative sizes are developed with the aim of balancing the computational efficiency and the prediction accuracy of the model. The performance of the MPS model when different grouping strategies are applied is investigated through the comparison of the predicted movement of soot particles generated from several materials. Based on this analysis a grouping strategy that results in the identification of three groups is shown to be sufficient to represent the influence of particle size on the gravitational settling for a variety of combustible materials and the computational cost of the extra governing equations for the transport of soot particles in the groups is acceptable. Furthermore, the efficiency of the model is demonstrated by simulating soot movement in a large-scale industrial building with a high ceiling

A Miniaturized In Situ Tensile Platform under Microscope

Aiming at the mechanical testing of three-dimensional specimens with feature size of centimeter level, a miniaturized tensile platform, which presents compatibility with scanning electron microscope (SEM) and metallographic microscope, was designed and built. The platform could accurately evaluate the parameters such as elastic modulus, elongation and yield limit, etc. The calibration experiments of load sensor and displacement sensor showed the two kinds of sensors had high linearity. Testing of transmission error and modal parameters showed that the platform presented good following behaviors and separation of resonance region. Comparison tests based on stress-strain curve were carried out between the self-made platform and the commercial tensile instrument (Instron) to verify the feasibility of the platform. Furthermore, the in situ tensile experiment under metallographic microscope was carried out on a kind of manganese steel

Analysis of the Driver’s Behavior Characteristics in Low Volume Freeway Interchange

Drivers’ behavior characteristics cannot be ignored in designing freeway interchange facilities in order to improve traffic safety. This paper conducted a field experiment in Qingyin expressway. Four freeway interchanges from K571+538 to K614+932 with relatively low volume were selected, and 12 qualified drivers, 6 car test drivers and 6 truck test drivers, were driving vehicles according to the driving program. GPS and eye-tracking instrument were employed to record running speed, real-time, running track, fixation point, and so forth. Box-plot graphs and Student’s t-test were used to analyze the 12 data sets of driver’s fixation on exit guide signs. Speed-distance curves of effective 11 data sets were plotted to examine the test drivers’ behavior in diverging area and merging area. The results indicated that (1) drivers recognize the exit direction signs in 170 m–180 m advanced distance; (2) the diverging influence area is 1000 m upstream of the diverge point, and the merging influence area is 350 m downstream of the merge point; (3) NO OVERTAKING sign is recommended to be placed at 350 m upstream of the diverge point. The results can provide guidance for the design of freeway interchange facilities and management in order to improve traffic safety

Image Based Computer-Aided Manufacturing Technology

Image based manufacturing technique is a novel manufacturing method, which is combine of machining technique and machine vision technique. By using the technique, machine tools can perform cutting process according to what they see, which is very like that the machine tool is equipped with "eyes". In this paper, some researches of author about the subject are proposed, and key techniques are included. Construction of image based manufacturing system is introduced briefly. The geometrical model is then built from the image information, in which process shape from shading with adaptive pro-processing method is used. After the model is built, cutting path is planed, and two cutting paths, line cutting and contour cutting, are conducted. NC programs are generated automatically, and machining process is then performed. Finally a prototype system named ImageCAM is introduced. Algorithms developed in our research are verified in the system

Increasing the Difference in Decision Making for Oneself and for Others by Stimulating the Right Temporoparietal Junction

The right temporoparietal junction (rTPJ) has been thought to be associated with the difference in self-other decision making. In the present study, using noninvasive transcranial direct current stimulation (tDCS), we examined whether stimulating the rTPJ could modulate the self-other decision-making difference. We found that after receiving anodal stimulation of the rTPJ, participants were more likely to choose a high-value item for others than for themselves in the situations where the win probability of the high-value item was equal to or greater than that of a low-value item, indicating that elevating the cortical excitability of the rTPJ might increase the self-other decision-making difference in certain decision contexts. Our results suggest that decision making for others depends on neural activity in the rTPJ and regulation of the excitability of the rTPJ can influence the self-other decision-making difference