Dibi (chongqing) Intelligent Technology Research Institute Co., Ltd.

Star Institute of Intelligent Systems

Yating Yang

Yongduan Song

Chunxu Ren

Junfeng Lai

Li Huang

Ziqiang Jiang

Shilei Tan

Huan Liu

Hong Long

Jie Zhang

Huan Chen

Congyi Zhang

Bowen Zhang

Gonglin Lu

Yujuan Wang

Mingyang Liu

Han Bao

Yingrui Chen

Kuilong Yang

Ge Song

The present disclosure provides a real-time vehicle overload detection method based on a convolutional neural network (CNN). The present disclosure detects a road driving vehicle in real time with a CNN method and a you only look once (YOLO)-V3 detection algorithm, detects the number of wheels to obtain the number of axles, detects a relative wheelbase, compares the number of axles and the relative wheelbase with a national vehicle load standard to obtain a maximum load of the vehicle, and compares the maximum load with an actual load measured by a piezoelectric sensor under the vehicle, thereby implementing real-time vehicle overload detection. The present disclosure has desirable real-time detection, can implement no-parking vehicle overload detection on the road, and avoids potential traffic congestions and road traffic accidents.

Real-time vehicle overload detection method based on convolutional neural network

The present disclosure discloses a neural network adaptive tracking control method for joint robots, which proposes two schemes: robust adaptive control and neural adaptive control, comprising the following steps: 1) establishing a joint robot system model; 2) establishing a state space expression and an error definition when taking into consideration both the drive failure and actuator saturation of the joint robot system; 3) designing a PID controller and updating algorithms of the joint robot system; and 4) using the designed PID controller and updating algorithms to realize the control of the trajectory motion of the joint robot. The present disclosure may solve the following technical problems at the same time: the drive saturation and coupling effect in the joint system, processing parameter uncertainty and non-parametric uncertainty, execution failure handling during the system operation, compensation for non-vanishing interference, and the like.

Neural network adaptive tracking control method for joint robots

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Chongqing, China

Yating Yang