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Advances in Modeling, Identification, and Control of Robotics
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Advances in Modeling, Identification, and Control of Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 7130

Special Issue Editors

Prof. Dr. Kai Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, and Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
Interests: robotics; motion control; non-linear control theory
Special Issues, Collections and Topics in MDPI journals
Dr. Dongdong Zheng

E-Mail Website
Guest Editor
School of Automation, Beijing Institute of Technology, Beijing 100081, China
Interests: deep learning; adaptive control theory; motion control
Dr. Yixiang Liu

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: tensegrity-based robot; bionic robot; special robot

Special Issue Information

Dear Colleagues,

Robot systems are expected to play a more significant role in more complex tasks in the coming years. This trend is present not only in industrial applications, but also in other areas, such as construction, agriculture, rehabilitation, surgeries, transportation, etc. The excellent performance of the robot system is pivotal to its successful implementation. However, the control performance is limited in many applications due to complex nonlinear robot dynamics and inaccurate model parameters. As a result, significant research efforts are required in order to develop novel dynamic models that consider robot flexibilities and nonlinearities, to enhance the identification accuracy of robot systems, and to develop high-performance control approaches, either by employing model-based or data-driven principles. This Special Issue will be dedicated to the latest research achievements, findings, and ideas in the modeling, identification, and control of robotics.

The topics of this Special Issue include, but are not limited to, the following areas:

  • New modeling methods of complex and nonlinear robot dynamics;
  • Fast and efficient computation of robot dynamics;
  • Parameter identification for complex robot dynamics;
  • Robot optimal control, adaptive control and intelligent control;
  • Data-driven robot learning and decision;
  • Design and development of robot actuators/sensors/systems for task-specific engineering applications.

Prof. Dr. Kai Guo
Dr. Dongdong Zheng
Dr. Yixiang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • obotics
  • parameter identification
  • modeling and control
  • data-driven learning
  • intelligent control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 7315 KiB  
Article
Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
by Caidong Wang, Yafeng Gao, Yapeng Xu, Xinjie Wang and Liangwen Wang
Appl. Sci. 2024, 14(7), 2685; https://doi.org/10.3390/app14072685 - 22 Mar 2024
Cited by 1 | Viewed by 1608
Abstract
In response to challenges like the complexity and limited scalability of existing variable stiffness joints, a novel variable stiffness joint, based on leaf spring elements, is introduced in this paper. The joint stiffness can be adjusted in real time by changing the effective [...] Read more.
In response to challenges like the complexity and limited scalability of existing variable stiffness joints, a novel variable stiffness joint, based on leaf spring elements, is introduced in this paper. The joint stiffness can be adjusted in real time by changing the effective length of the leaf spring via the use of an Archimedean spiral groove. The stiffness adjustment range and load capacity of the joint can be defined by manually configuring the number of springs involved during offline joint operations. A stiffness model for the joint is established based on the cantilever beam theory of material mechanics. The coupled effects of the design parameters of the variable stiffness mechanism on joint stiffness, elastic torque, and stiffness adjustment resistance torque are analyzed. A dynamic model for the joint is developed, while a PID controller is designed for simulation purposes. The motion characteristics of the joint are analyzed, confirming that this approach has certain advantages in terms of stiffness adjustment speed and accuracy. Full article
(This article belongs to the Special Issue Advances in Modeling, Identification, and Control of Robotics)
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23 pages, 22220 KiB  
Article
Pneumatic Bellows Actuated Parallel Platform Control with Adjustable Stiffness Using a Hybrid Feed-Forward and Variable Gain Integral Controller
by Martin Varga, Ivan Virgala, Michal Kelemen, L’ubica Miková, Zdenko Bobovský, Peter Jan Sincak and Tomáš Merva
Appl. Sci. 2023, 13(24), 13261; https://doi.org/10.3390/app132413261 - 14 Dec 2023
Viewed by 1511
Abstract
Redundant cascade manipulators actuated by pneumatic bellows actuators are passively compliant, rugged and dexterous, making them exceptionally well suited for application in agriculture. Unfortunately, the bellows are notoriously difficult to precisely position. This paper presents a novel control algorithm for the control of [...] Read more.
Redundant cascade manipulators actuated by pneumatic bellows actuators are passively compliant, rugged and dexterous, making them exceptionally well suited for application in agriculture. Unfortunately, the bellows are notoriously difficult to precisely position. This paper presents a novel control algorithm for the control of a parallel platform actuated by pneumatic bellows, which serves as a module of a cascade manipulator. The algorithm combines a feed-forward controller and a variable-gain I-controller. The mathematical model of the module, which serves as the feed-forward controller, was created by applying two simple regression steps on experimentally acquired data. The gain of the I-controller is linearly dependent on the total reference error, thereby addressing the prevalent problem of “a slow response or excessive overshoot”, which, in the described case, the simple combination of a feed-forward and constant-gain I-controller tends to suffer from. The proposed algorithm was experimentally verified and its performance was compared with two controllers: an ANFIS controller and a constant gain PID controller. The proposed controller has outperformed the PID controller in the three calculated criteria: IAE, ISE and ITAE by more than 40%. The controller was also tested under dynamic loading conditions, showing promising results. Full article
(This article belongs to the Special Issue Advances in Modeling, Identification, and Control of Robotics)
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21 pages, 12481 KiB  
Article
Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
by Chong Wu, Kai Guo and Jie Sun
Appl. Sci. 2023, 13(21), 11635; https://doi.org/10.3390/app132111635 - 24 Oct 2023
Cited by 6 | Viewed by 3033
Abstract
High-precision and low-overshoot force control are important to guarantee the material removal rate and surface quality of robot grinding. However, traditional force control methods are subjected to positional disturbance, stiffness disturbance, contact process nonlinearity, and force-position coupling, leading to difficulties in robot constant [...] Read more.
High-precision and low-overshoot force control are important to guarantee the material removal rate and surface quality of robot grinding. However, traditional force control methods are subjected to positional disturbance, stiffness disturbance, contact process nonlinearity, and force-position coupling, leading to difficulties in robot constant force control. Therefore, how to achieve smooth, stable, and high-precision constant force control is an urgent problem. To address this problem, a dual PID adaptive variable impedance control is established (DPAVIC). Firstly, PD control is used to compensate for the force error, and PID is used to update the damping parameters to compensate for the disturbance. Secondly, a nonlinear tracking differentiator is used to smooth the desired force and reduce the contact force overshoot. Then, the stability, convergence, and effectiveness of the force control algorithm are verified via theoretical analysis, simulations, and experiments. The force tracking error and overshoot of a conventional impedance controller (CIC), adaptive variable impedance control (AVIC), and DPAVIC are analyzed. Finally, the algorithm is used in grinding experiments on a thin-walled workpiece. The force tracking error is controlled within ±0.2 N, and the surface roughness of the workpiece is improved to Ra 0.218 μm. Full article
(This article belongs to the Special Issue Advances in Modeling, Identification, and Control of Robotics)
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