A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

The precision of initial rotor position detection is critical for the start and running performance of permanent magnet synchronous motor (PMSM). This work describes a new open loop approach for identifying the initial position of a PMSM with an incremental encoder, even when a constant load torque is being applied. By giving a testing current with high frequency to the stator winding, the initial rotor position of a PMSM can be detected with reasonable accuracy. The rotor almost does not move during the process of identification. The FFT algorithms are used to remove the phase bias effects in identification. Our approach is quicker and simpler than the conventional approaches

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl. In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Finite-Horizon Optimal Control of Discrete-Time Switched Linear Systems

Finite-horizon optimal control problems for discrete-time switched linear control systems are investigated in this paper. Two kinds of quadratic cost functions are considered. The weight matrices are different. One is subsystem dependent; the other is time dependent. For a switched linear control system, not only the control input but also the switching signals are control factors and are needed to be designed in order to minimize cost function. As a result, optimal design for switched linear control systems is more complicated than that of non-switched ones. By using the principle of dynamic programming, the optimal control laws including both the optimal switching signal and the optimal control inputs are obtained for the two problems. Two examples are given to verify the theory results in this paper

Guaranteed Cost Control for Multirate Networked Control Systems with Both Time-Delay and Packet-Dropout

Compared with traditional networked control systems, the sampling rates of the nodes are not the same in the multirate networked control systems (NCSs). This paper presents a new stabilization method for multirate NCSs. A multirate NCSs with simultaneous considering time-delay and packet-dropout is modeled as a time-varying sampling system with time-delay. The proposed Lyapunov function deceases at each input signal updating point, which is largely ignored in prior works. Sufficient condition for the stochastic mean-square stability of the multirate NCSs is given, and the cost function value is less than a bound. Numerical examples are presented to illustrate the effectiveness of the proposed control scheme

H

Scheduling policy based on model prediction error is presented to reduce energy consumption and network conflicts at the actuator node, where the characters of networked control systems are considered, such as limited network bandwidth, limited node energy, and high collision probability. The object model is introduced to predict the state of system at the sensor node. And scheduling threshold is set at the controller node. Control signal is transmitted only if the absolute value of prediction error is larger than the threshold value. Furthermore, the model of networked control systems under scheduling policy based on predicted error is established by taking uncertain parameters and long time delay into consideration. The design method of H∞ guaranteed cost controller is presented by using the theory of Lyapunov and linear matrix inequality (LMI). Finally, simulations are included to demonstrate the theoretical results

Cloning and characterization of microRNAs from wheat (Triticum aestivum L.)

A small RNA library was used to identify 58 miRNAs from 43 miRNA families from wheat (Triticum aestivum L.), and 46 potential targets were predicted

Ubiquitous Robotic Technology for Smart Manufacturing System

As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods