Article
Computer Science, Artificial Intelligence
Tairen Sun, Jiantao Yang, Yongping Pan, Hongliu Yu
Summary: This article proposes a model-based impedance learning control approach that can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. The proposed control approach guarantees the closed-loop control systems to be uniformly ultimately bounded and can be applied to physical human-robot interaction in repetitive tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Qing Sun, Shuai Guo, Sixian Fei
Summary: In order to address the collision problem between the manipulator links and the human upper limb, a null-space impedance control method based on a dynamic reference arm plane is proposed to achieve collision avoidance during human-robot physical interaction motion. The dynamic model and Cartesian impedance controller of the manipulator are established, and the null-space impedance controller of the redundant manipulator is then established based on the dynamic reference plane to prevent collision. Experimental results demonstrate the effectiveness of the proposed method in managing the null-space self-motion of the redundant manipulator and achieving collision avoidance during human-robot physical interaction motion. This research has significant potential in improving the safety and feasibility of motion-assisted training with rehabilitation robots.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2023)
Article
Mathematics
Hoang Vu Dao, Manh Hung Nguyen, Kyoung Kwan Ahn
Summary: This paper proposes a nonlinear functional observer (NFO) for the control design of robot manipulators. Compared to the well-known extended state observer (ESO) design, the proposed NFO has a simpler structure, more accurate estimations, and less computational effort, making it easier for practical implementation. Simulations are conducted to verify the effectiveness of the proposed algorithm in terms of both estimation performance and closed-loop control performance.
Article
Robotics
Chao Ren, Hongjian Jiang, Chaoxu Mu, Shugen Ma
Summary: This letter presents a conditional disturbance negation (CDN) control scheme for an omnidirectional mobile robot based on energy balance. The forces imposed on the robot are analyzed and classified, with the exploitation of the beneficial forces. A judgment function is designed to determine whether the disturbance is beneficial, allowing selective compensation. Convergence of the proposed control system is analyzed. Simulations and experimental results show that the proposed control scheme achieves better performance when disturbances contain beneficial parts.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Ting Wang, Jian Gao, Ou Xie
Summary: This paper proposes a method for accurately estimating contact force vectors in underwater teleoperation systems. The method utilizes a sliding mode disturbance observer and Q learning process to design a bilateral controller, and the effectiveness of the method is demonstrated through stability analysis and real experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Longxiang Wang, Chin-Yin Chen, Chongchong Wang, Kaichen Ying, Yanbiao Li, Guilin Yang
Summary: When the stiffness of the environment or people suddenly increases, the robot is prone to instability. This paper proposes an improved observer stabilization method to eliminate the influence of high-frequency noise and reduce misdiagnosis, and ensure the stable operation of the adaptive algorithm by updating the initial environment stiffness.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Automation & Control Systems
Shubo Wang, Jian Chen, Xiongkui He
Summary: An adaptive composite anti-disturbance attitude controller is proposed for agricultural quadrotor UAV to overcome disturbances in ultra-low altitude phenotype remote sensing and precision hovering of spraying. Experimental results demonstrate significant improvement in the controller's anti-disturbance performance under various disturbance conditions.
Article
Multidisciplinary Sciences
Jinghui Pan, Lili Qu, Kaixiang Peng
Summary: A data-driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed. The method introduces a Resnet-based fault diagnosis method to deal with various fault types of sensors and actuators. By stacking small convolution cores and increasing the core size, a deep residual network fault diagnosis model is derived. Simulation results show that the accuracy of fault diagnosis for robot system using DRNN is higher, and DRNN requires less model training time.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Tairen Sun, Long Cheng, Zengguang Hou, Min Tan
Summary: This paper introduces a tracking controller based on a sliding-mode disturbance observer (SMDOB) for a class of nonlinear systems with modeling uncertainties and external disturbances. The proposed controller guarantees semi-global asymptotic stability without the need for boundedness assumption of time derivatives of modeling uncertainties, and can be implemented with low complexity using only three parameters. Application to robot manipulators demonstrates the effectiveness of the SMDOB-based tracking control strategy.
SCIENCE CHINA-INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Danni Shi, Jinhui Zhang, Zhongqi Sun, Ganghui Shen, Yuanqing Xia
Summary: This paper investigates a novel disturbance observer and chattering-free adaptive sliding mode controllers for trajectory tracking of robot manipulators with unknown uncertainties and external disturbances. Simulation results demonstrate significant achievements in high-precision tracking performance and strong robustness.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Automation & Control Systems
Hanul Jung, Suhui Kwak, Hongsoo Choi, Sehoon Oh
Summary: This brief presents a novel tracking control algorithm for a micro-robot based on dynamics and positional measurements. The algorithm improves the tracking performance and response time by designing a two-degree-of-freedom control and a dual-rate state observer to address the dual-rate problem of micro-robot control systems.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Xing Liu, Shuzhi Sam Ge, Fei Zhao, Xuesong Mei
Summary: This article presents a novel interaction control method to address the optimized robot-environment interaction control problems in a flexible environment with unknown dynamics parameters. It defines a cost function, a complete state-space equation, and utilizes an improved Q-learning method to tackle the challenges brought by unknown environment dynamics and the reference position of the robot desired trajectory. Simulation and experimental studies confirm the validity of the proposed method.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Robotics
Johannes Lachner, Felix Allmendinger, Eddo Hobert, Neville Hogan, Stefano Stramigioli
Summary: This study investigates the certification process of applications with physical human-robot interaction (pHRI) and proposes controlling the robot's energy to ensure safety. By reducing the number of safety-related parameters, the proposed technique accelerates the commissioning of pHRI applications.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2021)
Article
Automation & Control Systems
Mou Chen, Shixun Xiong, Qingxian Wu
Summary: In this paper, a tracking flight control scheme based on a disturbance observer is proposed for a quadrotor with external disturbances. The scheme involves estimating unknown disturbances and developing flight controllers to track given signals. Experimental results demonstrate the effectiveness of the control strategy.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Kamran Maqsood, Jing Luo, Chenguang Yang, Qingyuan Ren, Yanan Li
Summary: In robot-assisted rehabilitation, researchers have developed a combined scheme of adaptive impedance control and trajectory learning to enhance rehabilitation performance and maintain a constant level of assistance. By utilizing an iterative trajectory learning approach, the robot reference is updated according to human movement in the direction of human movement, and an impedance adaptation method is used to compensate for unknown human force in the direction normal to the task trajectory. The proposed scheme has been tested in experiments emulating different upper-limb rehabilitation modes, showing the achievement of desired assistance level despite uncertainties in human dynamics.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Ran Jiao, Wenjie Liu, Ramy Rashad, Jianfeng Li, Mingjie Dong, Stefano Stramigioli
Summary: A novel end-effector bilateral rehabilitation robotic system (EBReRS) is developed for upper limb rehabilitation of patients with hemiplegia, providing simulations of multiple bimanual coordinated training modes, showing potential for application in home rehabilitation.
Article
Automation & Control Systems
Qiaosheng Pan, Yifang Zhang, Xiaozhu Chen, Quan Wang, Qiangxian Huang
Summary: A resonant piezoelectric rotary motor using parallel moving gears mechanism has been proposed and tested, showing high power output and efficiency.
