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Slewing control of Overhead Crane Based on State-enhance Coupling Method

Slewing Control of Overhead Crane Based on State-enhance Cou...

引用

第43届中国控制会议

作者： Rui Liu Biao Lu Jinyang Dong Weili Ding The 6th Research Institute of China Electronic Corporation Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Yanshan University National Key Laboratory of Key Technologies for Lifting Machinery Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Cranes which are frequently utilized as sizable machinery, are widely used across a range of settings, including warehouses, distribution hubs, harbors, steel plants, and manufacturing facilities. However, the current researches on cranes have paid insufficient attention to the rotation of loads during transportation. Additionally, some of the proposed control methods fail to effectively integrate information from both actuated DOFs(degrees of freedom) and unactuated DOFs, leading to a loss of controller robustness. In this paper, we establish an overhead crane model with a slewing mechanism, focusing on the analysis of crane's lifting and rotating motion. Based on that, a nonlinear controller is proposed for load slewing control. The stability of the controller is proven using Lyapunov techniques and LaSalle's invariance theorem. The effectiveness and robustness of the controller are validated through numerical simulations.

关键词： Load Rotation Nonlinear controller Robustness

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TICNet: Three-Branch Real-Time Semantic Segmentation Network with Intensive Compensation of Railway Track Scenes

TICNet: Three-Branch Real-Time Semantic Segmentation Network...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Yiwen Bai Lu Yang Lei Zhang Yajing Song School of Transportation Science and Engineering Beihang University Beijing China Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China Traffic Control Technology Co. Ltd Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

With the rapid development of railway traffic system, real-time semantic segmentation plays a crucial role in railway track scene monitoring. However, most of the existing methods use lightweight convolution to reduce the computational complexity, which results in reduced accuracy. To solve this problem, this paper proposes a real-time semantic segmentation network called TICNet, which is specifically used for accurate segmentation of railway track scenes. TICNet uses a novel three-branch structure designed for efficient feature extraction and accurate segmentation. Results on the RailSem19 dataset show that TICNet has a segmentation accuracy of 62.36% mIoU with an inference speed of 81.2 FPS, which is significantly better than many existing methods.

关键词： Accuracy Convolution Semantic segmentation Semantics Focusing Machine learning Feature extraction Real-time systems Computational complexity Monitoring

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images

Interpretable analysis of feature importance and implicit co...

引用

Industrial Cyber-Physical systems (ICPS)

作者： Xiaohu AO Feng Wang Juan Zhao Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. There have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. The interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： Deep learning Analytical models Correlation Image recognition Neural networks Thumb Gray-scale

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A control Method of Robotic Arm Based on Improved Deep Deterministic Policy Gradient

A Control Method of Robotic Arm Based on Improved Deep Deter...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yanpeng Shao Haibo Zhou Shuaishuai Zhao Xiaoyan Fan Jiayi Jiang National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China

With the continuous development of Machine Learning, Reinforcement Learning has achieved excellent results in many fields. An improved Deep Deterministic Policy Gradient (DDPG) algorithm is proposed in this paper to optimize Reinforcement Learning control performance, because of the issue of sparse rewards during training in continuous action spaces such as robotic arms, leading to slow convergence and low success rates. The algorithm merges the DDPG algorithm with the Hindsight Experience Relay (HER) algorithm, enabling the agent to learn from the failed experience when learning. Moreover, a double experience replay buffer is introduced, comprising a primary buffer and a secondary buffer containing high-reward experiences, which enhances the sampling probability of high-quality samples. At long last, a PyBullet simulation environment was utilized to execute a simulation experiment. The robotic arm using the improved DDPG algorithm achieved a success rate of nearly 100% in about 220 epochs of training, and the algorithm convergence speed was greatly improved. The trained robotic arm can reach any target point in the workspace. The efficacy of the improved DDPG algorithm is demonstrated by the outcomes, providing a valuable reference for further exploration into the intelligent control of robotic arms for object grasping.

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Dimensional synthesis and static/dynamic analysis of a robot in automobile hubs polishing

Dimensional synthesis and static/dynamic analysis of a robot...

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IEEE International Conference on Mechatronics and Automation

作者： Long Chen Manru Xi Yue Ma Bin Li Qi Liu Changrui Li Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Focusing on the polishing operation of automobile hubs, this paper proposed a polishing robot by combining a 1T2R parallel mechanism (PM) and an X-Y worktable. The dimensional synthesis and static/dynamic performance analysis of the 1T2R PM are also presented with the key points as follows. Firstly, the inverse displacement model and velocity model of the PM are built to formulate the motion/force transmissibility indices on the basis of the generalized Jacobian matrix. Secondly, the dimensional synthesis problem is formulated to determine the optimized normalized dimensional parameters by maximizing the global force/motion transmissibility of the PM subject to geometric constraints without mechanical interference. Finally, the finite element (FE) model of the 1T2R PM is built with the aid of the SAMCEF, and the static stiffness and lower-order natural frequencies of the PM are evaluated. The research results of this paper provide dimensions and mechanical structures for developing the physical prototype of the automobile hub polishing robot.

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Structure design and kinematics modeling of a new continuum parallel robot

Structure design and kinematics modeling of a new continuum ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Xinjie Wang Bin Li Yue Ma Wei Peng Pai Peng Jinyuan Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Compared with the traditional rigid robots, the continuum robot has been widely studied because of its advantages of compact design and flexible motion. The design of continuum parallel robots are similar to that of the traditional rigid-link parallel robot, and the precision, stability and strength characteristics of rigid-link parallel robots are combined with the compactness and flexibility characteristics of continuum robots. In recent research advances, it is well appreciated to suggest that the use of parallel continuum robots with passive supports is supported by elastic rods. The passive branch undergoes deformation along its entire length and uses complex formulas to describe its shape and kinematics. According to different methods, this paper proposes the design of a parallel robot dual continuum translation passive branch chain. This continuum joint will assume a constant curvature bend and use this assumption for kinematic modeling. Since the continuum joint is a flexible joint that utilizes the characteristics of new materials, it uses material deformation to replace the motion pair, which successfully realizes that the passive branch joint can replace the universal joint and the ball hinge in the parallel robot, and the joint can be bent more than 90 0 to achieve a larger range of workspace. Each passive branch of the new continuum robot is a continuum structure composed of two mutually coupled flexible hinge joints, and the translational motion of the end effector is inherently realized by using its material properties. The paper includes a new mechanism description and kinematics modeling.

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Gait Planning and Simulation of 3-RRP Legged Mobile Robot

Gait Planning and Simulation of 3-RRP Legged Mobile Robot

引用

IEEE International Conference on Mechatronics and Automation

作者： Jinyuan Wang Bin Li Yue Ma Xinjie Wang Pai Peng Wei Peng Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

In order to meet the requirements of keeping the body posture level with the ground during the movement of the quadruped robots, this paper designs a quadruped robot with three joints and three degrees of freedom in each leg, which is a 3-RRP kinematic chain. The legs and body of the quadruped robot are virtually modeled by SolidWorks. Combining the forward and inverse kinematics analysis, the reasonable and ideal foot trajectory and body motion trajectory are planned in Matlab. The gait stability is based on the ZMP stability criterion and is quantified by the stability margin. According to the structural characteristics of the quadruped robot in this paper, the body posture adjustment strategy is proposed. Finally, the simulation is carried out under the two gait plannings of straight driving and turning using ADAMS. The simulation results show that the quadruped robot has a reasonable walking gait on the flat road surface with little impact on the body. The minimum stability margin of the strategy with body adjustment is improved compared with the strategy without body adjustment.

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Accurate MTPA control Based on Ferrari Method for IPMSM with Partial Derivative Term Calculation

Accurate MTPA Control Based on Ferrari Method for IPMSM with...

引用

International Conference on Electrical Machines and systems (ICEMS)

作者： Yiyang Li Xin Gu Zhiqiang Wang Liyan Guo Wei Chen Xinmin Li Chen Li School of Electronics and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin China School of Electrical Engineering Tiangong University Tianjin China National Local Joint Engineering Research Center of Electrical Machine System Design and Manufacturing China Zhejiang University Advanced Electrical Equipment Innovation Center Hangzhou China

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

Even if the exact motor parameters are known, the MTPA strategy cannot obtain precise maximum torque per ampere (MTPA) points under large current amplitude because the partial derivative of inductance to current is ignored in the modeling process. In view of this, the MTPA mathematical model considering the partial derivative of inductance to current is established in this paper, and the MTPA point tracking error caused by ignoring the partial derivative in the modeling process based on Ferrari method is analyzed. Then, an offline-online combined method for calculating the partial derivative term is proposed to compensate the MTPA tracking error. On this basis, an MTPA control strategy based on Ferrari method considering the partial derivative of inductance to current is proposed. Experiment to verify the feasibility and effectiveness of the proposed method.

关键词： Inductance Analytical models Torque Accuracy Permanent magnet motors Mathematical models Trajectory

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intelligent Diagnosis of Planetary Gearboxes Based on DAE-CNN

Intelligent Diagnosis of Planetary Gearboxes Based on DAE-CN...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Tianbo Kang Mian Zhang Ruitong Xie Jiwei Chen Hongbiao Xiang Yuhang Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology) Tianjin China

Planetary gearboxes (PGs) are the core transmission components of complex rotating machinery. The diverse forms of internal gear faults often complicate diagnosis and maintenance work. To improve the fault diagnosis accuracy of PGs, this paper proposes a method for diagnosing PG faults based on deep autoencoders (DAE) and convolutional neural networks (CNN). The method fully utilizes the deep learning capability of DAE and combines it with the precise recognition ability of CNN for fault features. Through precise learning in the encoding layer, it achieves deep dimensionality reduction of fault features and further learns these features after dimensionality reduction by CNN. Compared to traditional CNN, the method reduces the computation parameters and resource consumption, resulting in higher precision intelligent fault diagnosis. Experimental results demonstrate that DAE-CNN exhibits stronger fault diagnosis ability than traditional networks.

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A Model-Data Compound Driven Method For Compensating Robot Tracking Error

A Model-Data Compound Driven Method For Compensating Robot T...

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IEEE International Conference on Mechatronics and Automation

作者： Pai Peng Qi Liu Bin Li Wei Peng Xinjie Wang Jinyuan Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Industrial robots are widely used in industrial production because of their high work efficiency and high flexibility, but their low tracking accuracy makes them unable to be applied on a large scale in high-precision manufacturing. Considering the modeling error of robot, the problem of low tracking accuracy should be solved by combining model-based methods and data-based methods. Thus, a model-data compound approach is proposed for compensating robot tracking error. This method comprehensively considers two error compensation methods of model-driven and data-driven, which constructed the input matrix by using the model-based information and the data-based information. The former includes the desired position, velocity, acceleration, torque and friction torque; and the latter includes the actual torque. For the purpose of predicting the correlation between the joint error and the historical joint motion state, the historical information of the joint motion is also added in the input matrix for improving the error prediction accuracy. Then, a convolutional neural network prediction model is constructed for predicting and compensating the tracking error of each actuated joint. The experimental results show that the proposed method has good prediction accuracy and compensation effect. Compared to joint control without using the proposed method, the root mean square of joint tracking errors was reduced by up to more than 69%.

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