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Electronics, Energy systems and Power Engineering (EESPE), IEEE International Conference on

作者： Kun Liang Hang Zhang School of Management 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 National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep $\mathbf{Q}$ network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction.

关键词： Training Accuracy Costs Computational modeling Customer satisfaction Demand forecasting Clustering algorithms Predictive models Scheduling Logistics

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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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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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An optimal design and fuzzy control method for a new flapping-wing robot

An optimal design and fuzzy control method for a new flappin...

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Electrical Engineering and control Science (IC2ECS), International Conference on

作者： Chunxiao Zhang Yansen Zhao Ting Zhang Qi Liu Bin Li Yue Ma Mengze Suo 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

Bionic flapping-wing robot has the advantages of good maneuverability, low noise and strong stealth performance. It is widely used in military intelligence collection, field rescue and investigation. In this paper, an optimal design method of bionic flapping-wing robot non-phase difference driving mechanism is proposed. By setting the constraints of flapping angle amplitude, fuselage height and slider stroke, the scale parameters of the mechanism are optimized by using the minimum transmission angle; on this basis, the effectiveness of fuzzy PD control in improving the tracking accuracy of the drive system is verified by means of ADAMS and MATLAB / Simulink electromechanical coupling simulation. This research has practical value for designing new flapping-wing robot and improving its control quality.

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control Strategy of Permanent Magnet Synchronous Motor and Brushed DC Servo Motor Based on ADRC

Control Strategy of Permanent Magnet Synchronous Motor and B...

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IEEE International Conference on mechatronics and Automation

作者： Liang Liu Haikui Liu Guangyin Nie Yang Gao 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

Adaptive disturbance rejection control (ADRC) is a rapidly evolving and promising control method that has been widely applied in various industrial fields and may yield better results than traditional method. In order to promote the controller performance, ADRC technology was employed to construct a three-loop structure for the controllers of permanent magnet synchronous motor (PMSM) and brushed DC Servo Motor (BDSM). Firstly, the mathematical models of the both kinds of motors were presented and combined with the ADRC, respectively. Secondly, the simulation models based on ADRC were constructed in MATLAB/SIMULINK, respectively. Finally, comparative experiments were carried out by ADRC and Proportional-Integral (PI) methods. The simulation results showed that ADRC outperforms PI control in terms of response speed, overshoot reduction, and anti-interference capability, which verified the proposed method can exhibit superior dynamic properties in the induction motor control.

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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...

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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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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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PATH OPTIMIZATION FOR DYNAMIC OBSTACLE AVOIDANCE OF PIONEER ROBOT BASED ON DDPG SMART AGENT

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UPB Scientific Bulletin, Series D: Mechanical Engineering 2023年第2期85卷 29-42页

作者： Zheng, Qingchun Peng, Zhi Zhu, Peihao Zhao, Yangyang Ma, Wenpeng Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China China School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

To improve the path efficiency for dynamic obstacle avoidance algorithms of Pioneer robots, this paper proposes an approach to create deep deterministic policy gradient (DDPG) smart agents with recurrent neural networks (RNN) to optimize the path strategy. This approach is realized by training RNN with the backpropagation (BP) algorithm to train the agent. First, the SolidWorks model and obstacle avoidance model of the Pioneer robot is established. The LIDAR sensor is used to obtain environmental information. Further, deep reinforcement learning obstacle avoidance algorithm based on RNN is presented. The dynamic obstacle avoidance path of the Pioneer robot is further simulated in two different virtual robot experiment platform (V-REP) scenarios. The simulation results show that the proposed algorithm shortens the training time by 26.3% compared with the DDPG algorithm. Our proposed scheme can improve the path efficiency for dynamic obstacle avoidance of the Pioneer robot. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Reinforcement learning

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Structure design and kinematics modeling of a new continuum parallel robot

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

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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

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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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