Qingdao University of Technology

Huawei Technologies Co., Limited

Xinchao Liu

Haomian Zheng

Yufei Wang

Zhicheng Sui

Lvchuan Zhang

Chuanjun Wu

Jingbo Zhao

Jingxuhui Zhu

Tengfei Qiu

Bingxin Xue

Zhong Wang

Xiaohan Zhang

Wenbin Zhang

The disclosure relates to a grabbing detection method based on an RP-ResNet. The method comprises: inputting an image of a target; pre-processing data; performing data processing by an RP-ResNet model; and finally, generating a grabbing block diagram of a target to be grabbed. On the basis of a model ResNet 50, a region proposal network is used in the 30th layer of a network, fuzzy positioning is performed on the positions of grabbing points, feature information of high and low layers is fully fused to strengthen the utilization of information of low layers, and the 40th layer of the network is added to an SENet structure, thereby further increasing the detection accuracy of a grabbing points. By a grabbing detection framework based on ResNet-50, a residual network, a region proposal idea and SENet are combined, thereby ensuring rapid and accurate target detection.

Grabbing detection method based on RP-ResNet

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

Networked control system time-delay compensation method based on predictive control

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Shandong, China

Xinchao Liu