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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the robustness of permanent magnet synchronous motor (PMSM) servo system, a composite controller based on the combination of improved expanded state observer (ESO) and sliding mode control (SMC) was proposed. First, and SMC based on a novel convergence law was proposed, which introduced a system state variable in the novel convergence law so that the convergence speed could be related to the system state, ensuring that the system state reached the sliding mode surface and converged to zero in a finite time. Secondly, the ESO was utilized to estimate the system state and offset the external perturbations, and a new fal function was proposed to address the problems of the traditional fal function which was prone to cause system jitter and large system gain when the error was large. Finally, the simulation was carried out in Matlab/Simulink. The simulation results show that compared with the system using the traditional sliding mode controller, the response time of the system using the new controller is 0.042 s faster, and there is a better anti-disturbance performance when the load disturbance is added,therefore,this system has a better dynamic performance and control accuracy [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of applying deep reinforcement learning(DRL)to realize autonomous behavioral decision-making of robotic arms, the high-dimensional continuous state-action space is prone to low data sampling efficiency and low quality of empirical samples, which ultimately leads to slow convergence of the reward function and long learning time. To address this problem, a DRL model that introduces prior knowledge was proposed. The model was combined with the inverse kinematics of the robotic arm, and prior knowledge was introduced to guide the agent during the sampling phase of DRL, addressing the issues of low data sampling efficiency and poor quality of experience samples during the learning process. Furthermore, the introduced prior knowledge DRL model’s strong generalization capabilities were verified when facing new tasks through network parameter transfer. Lastly, joint simulation experiments were conducted using Python and the CoppeliaSim platform. The results show that the DRL model with the introduction of prior knowledge improves the learning efficiency by 13.89% and 12.82%,and the success rate of completing the task increases by 16.92% and 13.25% than the original model; in the new task, the learning rate improves by 23.08% and 23.33%,and the success rate improves by 10.7% and 11.57%. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An improved D* algorithm was proposed to solve the problems of low efficiency and complex inflection points in three-dimensional path autonomous planning of unmanned aerial vehicle using traditional D* algorithm. Firstly, variable step size was used to expand nodes, which speeded up the algorithm’s traversal of nodes in the planning stage. At the same time, the fusion formula of Chebyshev distance and Manhattan distance was used as surrogate value, the cost function was improved, the calculation amount of the algorithm was reduced, and the low efficiency of the algorithm was solved. Finally, the three-dimensional map and the obtained path were projected onto the two-dimensional plane, key nodes were selected, redundant nodes were discarded, and the generated path was optimized. The number of inflection points and complexity of the obtained path were reduced. The simulation results show that the improved D* algorithm can be effectively used in the path planning of unmanned aerial vehicle in the three-dimensional environment, and the expected effect is achieved in terms of accelerating the efficiency of the algorithm and improving the quality of the path. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Jiangsu University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the control precision and anti-disturbance performance of the flexible manipulator, a combined control method based on improved active disturbance rejection control and sliding mode control was proposed. Firstly, the dynamic model was constructed by the Lagrange method and assumed mode method, and then the flexible arm was decoupled by singular perturbation theory to obtain the slow-varying and fast-varying subsystems. For the slow-varying subsystem, the improved active disturbance rejection control was used to achieve trajectory tracking, and a new fal function was constructed to solve the problems that the inflection point of the fal function was not smooth, which easily caused the system chattering and the system gain was large when the error was large. At the same time, sliding mode control was used to replace the nonlinear state error feedback control, a new reaching law was adopted in order to improve the control quality of sliding mode control. For fast-varying subsystem, sliding mode control was used for vibration suppression. The Matlab simulation results show that the combined control method has better trajectory tracking performance, anti-disturbance performance and vibration suppression performance than the traditional active disturbance rejection control. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)