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Actuators
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Intelligent Control of Flexible Manipulator Systems and Robotics
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Intelligent Control of Flexible Manipulator Systems and Robotics

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 13549

Special Issue Editors

Dr. Xiuyu He

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: distributed parameter systems; intelligent control; vibration control; flexible systems; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhijia Zhao

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China
Interests: robotics; adaptive control and learning control; flexible systems

Special Issue Information

Dear Colleagues,

The ever-growing utilization of flexible manipulators and robotics in various applications has been motivated by the requirements and demands of industrial automation. The flexural dynamics (vibration) in flexible manipulators and robotics have been the main research challenge in the control of such systems. However, traditional control methods cannot achieve excellent performance in vibration suppression and dynamic responses. In recent years, many intelligent control methods have been proposed and achieved good development, which provides the possibility for the intelligent control of flexible manipulators. Accordingly, this Special Issue seeks to collect theoretical results about the intelligent control of flexible manipulator systems and robotics and experimental studies on their use in real-world applications.

Authors are invited to contribute to the Special Issue by submitting original papers that are related but not limited to:

  1. The modeling and identification of flexible manipulator systems and robotics;
  2. The mechanism design, fabrication and optimization of flexible manipulators and robotics;
  3. Intelligent control for flexible manipulator systems and robotics;
  4. Intelligent control for autonomous systems;
  5. Machine learning methods for flexible manipulator systems and robotics;
  6. Applications of intelligent-control-based flexible manipulator systems and robotics in industry/agriculture/logistics/medicine;
  7. Summaries of the latest intelligent control theories for flexible manipulator systems and robotics.

Dr. Xiuyu He
Dr. Zhijia Zhao
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. Actuators is an international peer-reviewed open access monthly 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

  • Flexible manipulator systems
  • Flexible robotics
  • Vibration control
  • Robust control
  • Adaptive control
  • Neural network control
  • Fuzzy control
  • Optimal control
  • Machine learning
  • Actuator dynamics and control

Published Papers (6 papers)

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Research

21 pages, 2146 KiB  
Article
Forced Servoing of a Series Elastic Actuator Based on Link-Side Acceleration Measurement
by Zhuo Wang, Shenghong Liu, Bo Huang, Haowu Luo and Feiyan Min
Actuators 2023, 12(3), 126; https://doi.org/10.3390/act12030126 - 15 Mar 2023
Cited by 1 | Viewed by 1710
Abstract
Joint stiffness of an elastic-joint robot can be changed according to joint stiffness requirements. A series elastic actuator (SEA) can reduce the contact stiffness between the body and the environment or human, which can further ensure interactive operation in a human–machine-compatible environment. However, [...] Read more.
Joint stiffness of an elastic-joint robot can be changed according to joint stiffness requirements. A series elastic actuator (SEA) can reduce the contact stiffness between the body and the environment or human, which can further ensure interactive operation in a human–machine-compatible environment. However, the introduction of the SEA improves the complexity of the robot dynamics model. In this paper, we propose a control schema based on link-side acceleration measurement to eliminate the overshoot and vibration in the transient process of force control. An extended Kalman filter (EKF) algorithm that fuses photoelectric encoders and accelerometers is first presented based on the link-side acceleration measurement. Following this, based on the external torque estimation, the vibration reduction control algorithm is designed. The simulation model is built, and the algorithm design and simulation of position control and force control are carried out and finally tested on the real robot platform. The effectiveness of the control algorithm is proved. The experimental results show that the dynamic response of the external force estimation is about 2 ms faster than that of the force sensor, and the error between the estimated external torque and the real external torque is within ±0.16 N·m. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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13 pages, 565 KiB  
Article
Adaptive Neural Control of a 2DOF Helicopter with Input Saturation and Time-Varying Output Constraint
by Bing Wu, Jiale Wu, Jian Zhang, Guojian Tang and Zhijia Zhao
Actuators 2022, 11(11), 336; https://doi.org/10.3390/act11110336 - 18 Nov 2022
Cited by 6 | Viewed by 1676
Abstract
An adaptive neural control for uncertain 2DOF helicopter systems with input saturation and time-varying output constraints is provided. A radial basis function neural network is used to estimate the uncertainty terms present in the system. The saturation error and the external disturbance are [...] Read more.
An adaptive neural control for uncertain 2DOF helicopter systems with input saturation and time-varying output constraints is provided. A radial basis function neural network is used to estimate the uncertainty terms present in the system. The saturation error and the external disturbance are considered as a composite disturbance, and an adaptive auxiliary parameter is introduced to compensate it. An asymmetric barrier Lyapunov function is employed to address the constraint violation of the system output. The closed-loop stability of the system is then demonstrated by Lyapunov theory analysis. Simulation results demonstrate the effectiveness of the control strategy. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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21 pages, 832 KiB  
Article
Adaptive Robust Tracking Control for Near Space Vehicles with Multi-Source Disturbances and Input–Output Constraints
by Xiaohui Yan, Guiwei Shao, Qingyun Yang, Liang Yu, Yuwu Yao and Shengxia Tu
Actuators 2022, 11(10), 273; https://doi.org/10.3390/act11100273 - 23 Sep 2022
Cited by 2 | Viewed by 1341
Abstract
In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network [...] Read more.
In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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17 pages, 2410 KiB  
Article
AILC for Rigid-Flexible Coupled Manipulator System in Three-Dimensional Space with Time-Varying Disturbances and Input Constraints
by Jiaming Zhang, Xisheng Dai, Qingnan Huang and Qiqi Wu
Actuators 2022, 11(9), 268; https://doi.org/10.3390/act11090268 - 19 Sep 2022
Cited by 2 | Viewed by 1742
Abstract
In this paper, an adaptive iterative learning control (AILC) law is developed for two-link rigid-flexible coupled manipulator system in three-dimensional (3D) space with time-varying disturbances and input constraints. Based on the Hamilton’s principle, a dynamic model of a manipulator system is established. The [...] Read more.
In this paper, an adaptive iterative learning control (AILC) law is developed for two-link rigid-flexible coupled manipulator system in three-dimensional (3D) space with time-varying disturbances and input constraints. Based on the Hamilton’s principle, a dynamic model of a manipulator system is established. The conditional equation that is coupled by ordinary differential equations and partial differential equations is derived. In order to achieve high-precision tracking of the revolving angles and vibration suppression of the elastic part, the iterative learning control law based on the disturbance observer is considered in the process of the design controller. The composite Lyapunov energy function is proposed to prove that the angle errors and elastic deformation can eventually converge to zero with the increase of the number of iterations. Ultimately, the simulation results to rigid-flexible coupled manipulator system are given to prove the convergence of the control objectives under the adaptive iterative learning control law. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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24 pages, 6395 KiB  
Article
An Improved Proximal Policy Optimization Method for Low-Level Control of a Quadrotor
by Wentao Xue, Hangxing Wu, Hui Ye and Shuyi Shao
Actuators 2022, 11(4), 105; https://doi.org/10.3390/act11040105 - 6 Apr 2022
Cited by 4 | Viewed by 3250
Abstract
In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor. The attitude and position information of the quadrotor is directly mapped to the PWM signals of [...] Read more.
In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor. The attitude and position information of the quadrotor is directly mapped to the PWM signals of the four rotors through neural network control. To constrain the size of policy updates, a PPO algorithm based on Monte Carlo approximations is proposed to achieve the optimal penalty coefficient. A policy optimization method with a penalized point probability distance can provide the diversity of policy by performing each policy update. The new proxy objective function is introduced into the actor–critic network, which solves the problem of PPO falling into local optimization. Moreover, a compound reward function is presented to accelerate the gradient algorithm along the policy update direction by analyzing various states that the quadrotor may encounter in the flight, which improves the learning efficiency of the network. The simulation tests the generalization ability of the offline policy by changing the wing length and payload of the quadrotor. Compared with the PPO method, the proposed method has higher learning efficiency and better robustness. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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16 pages, 2085 KiB  
Article
Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood
by Haixing Wang, Yi Yang, Zhiwei Lin and Tian Wang
Actuators 2022, 11(4), 99; https://doi.org/10.3390/act11040099 - 25 Mar 2022
Cited by 1 | Viewed by 2264
Abstract
In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a [...] Read more.
In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum a posteriori based on the hidden Markov random field model. The convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism. The effectiveness of the method is verified by the case study of the top-coal caving. The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile, the top-coal caving performance can be improved significantly. Full article
(This article belongs to the Special Issue Intelligent Control of Flexible Manipulator Systems and Robotics)
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