Di‐Hua Zhai

Beijing Institute of Technology

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Yuanqing Xia

Beijing Institute of Technology

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Sheng Yu

Beijing Institute of Technology

Yuhan Xiong

Shandong University of Science and Technology

Sihua Zhang

Beijing Institute of Technology

Yu Kang

University of Science and Technology of China

Yongkang Wang

Beijing Institute of Technology

Ping Zhao

China Three Gorges University

Haocun Wu

The First People's Hospital of Shunde

Yuyin Guan

China Building Materials Academy

Zhijun Li

Dalian University of Technology

Cooperative Institutions

Beijing Institute of Technology

Chinese Academy of Sciences

Capital Medical University

Beijing Tian Tan Hospital

China United Network Communications Group (China)

University of Science and Technology of China

Huazhong University of Science and Technology

Tianjin University

Beihang University

Shanghai Jiao Tong University

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Finite-time adaptive anti-windup teleoperation control

CRC Press eBooks (2023)

Di‐Hua Zhai Yuanqing Xia

Teleoperation

10.1201/9781003382058-9

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MTGrasp: Multiscale 6-DoF Robotic Grasp Detection

IEEE/ASME Transactions on Mechatronics (2024)

Sheng Yu Di‐Hua Zhai Yuanqing Xia

The actual grasping scenarios are often complex, and achieving reliable and stable robot grasping in complex scenes is an important problem. Due to its high flexibility, 6-DoF grasping methods are well-suited for representing grasping poses in complex scenes. In order to achieve more accurate 6-DoF grasping, this article proposes a new multiscale 6-DoF grasp pose detection method. First, based on the object model, we can generate multiple grasp poses on the object model. Evaluating the quality of the grasps in a reasonable and effective way is a key issue. To achieve more accurate grasp pose evaluation, we propose a multimetric grasp pose evaluation algorithm to assess grasp poses more accurately and assign a grasp quality score. The neural network learns and detects the best grasp poses based on the grasp quality score. Next, considering the existence of objects at multiple scales in practical scenes, we propose a multiscale grasp pose search algorithm to detect grasp candidate regions and search for multiscale grasp poses. Based on the proposed algorithms, we construct a new 6-DoF grasp pose detection network called MTGrasp and train/test it on a large-scale grasp dataset, GraspNet-1Billion dataset. Experimental results show that our proposed algorithm achieves state-of-the-art performance on the dataset. Finally, we apply MTGrasp to actual grasping experiments with the UR3 robot, resulting in a higher grasp success rate.

Robotic hand

10.1109/tmech.2024.3391917

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H ∞ control for heat exchanger systems modeled by Markovian jump bilinear systems with saturating actuator and time delays

Chinese Control Conference (2011)

Yu Kang Yi Liu Ping Zhao Di‐Hua Zhai

In this paper, the control for the heat exchanger systems is related. According to the traits of the heat exchanger system, a class of Markovian jump bilinear systems with saturating actuator and tim-varying delays is derived. Then the problem of delay-dependent H ∞ stabilization control for this class of Markovian jump bilinear systems is considered. Sufficient conditions for the delay-dependent stability of the closed-loop system via a state feedback control are developed by using stochastic Lyapunov-Krasovskii approach and the H ∞ stabilization control is designed to make the stochastic stable system achieve γ disturbance attenuation. An estimatiton for the domain of attraction of the origin can also be obtained. An illustrative numerical example is provided to demonstrate the effectiveness of the proposed approach.

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Synthetic Depth Image-based Category-Level Object Pose Estimation with Effective Pose Decoupling and Shape Optimization

IEEE Transactions on Instrumentation and Measurement (2024)

Sheng Yu Di‐Hua Zhai Yuanqing Xia

Decoupling (probability)

10.1109/tim.2024.3427799

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Finite-Time Control of Teleoperation Systems With Input Saturation and Varying Time Delays

IEEE Transactions on Systems Man and Cybernetics Systems (2016)

Di‐Hua Zhai Yuanqing Xia

This paper develops a finite-time control approach for nonlinear teleoperation systems, which is capable of unifying the study of model uncertainties, actuator saturation, and asymmetric time-varying delays in the same framework. First, a novel anti-windup compensator is designed to analyze the effect of actuator saturation. To achieve the finite-time tracking, a nonsmooth generalized switched filter is also investigated. By introducing the anti-windup compensator and the generalized switched filter into the adaptive fuzzy control torque design, a novel finite-time controller is developed. By using the multiple Lypaunov-Krasovskii functionals method, the resulting closed-loop system is state-independent input-to-output practical stable (SIIOpS) and based on this, it is proved to be finite-time SIIOpS. It is shown that the asymptotic convergence of the adaptive estimation errors and the finite-time convergence of the position tracking errors are obtained, whether the robots contact with the human operator/environment or not. Finally, the effectiveness is demonstrated by the simulation results.

Teleoperation

10.1109/tsmc.2016.2631601

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Citations (77)

An sEMG-Driven Identification of Human Upper Limb Stiffness and Its Application in Variable Impedance Control

Leyun Hu Tinghan Xu Ziyun Zhao Di‐Hua Zhai Yuanqing Xia

In this paper, we try to estimate human muscle stiffness in a static estimation frame and transfer it to Baxter by space conversion to realize a variable impedance control. In this frame, a neural network is designed to estimating the end force, which is mapped with human upper limb stiffness by the TVSEM model. In this way, human stiffness can be estimated at a specific posture with different force export. Then, the stiffness will be transferred to the robot control strategy and realize posture-limited soft control. For the sake of estimating endpoint force of human upper limb by sEMG efficiently, we test two classical neural network models, Dilated convolutional neural network(DCNN) and Squeeze-and-Excitation neural network(SEnet). To improve the estimated capability, we design a new compound model named Dilated-Convolution Squeeze-and-Excitation neural network(DC-SE). We compared them in several different situations, which contain estimating different quantities of category or estimating the same categories but use shorter or longer sample lengths. To confirm the efficiency of this muscle stiffness estimation frame, we apply it in a variable impedance control experiment. Human upper limb stiffness is estimated and transferred from stiffness space to joint space by space conversion for impedance control needs. And the result of the experiment proves that the method could estimate joint stiffness reliably.

Joint stiffness

Impedance Control

Mechanical impedance

10.23919/ccc52363.2021.9550092

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Structural controllability of networked relative coupling systems

Automatica (2021)

Yuan Zhang Yuanqing Xia Di‐Hua Zhai

10.1016/j.automatica.2021.109547

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Cooperative Online Learning for Multiagent System Control via Gaussian Processes With Event-Triggered Mechanism

IEEE Transactions on Neural Networks and Learning Systems (2024)

Xiaobing Dai Zewen Yang Sihua Zhang Di‐Hua Zhai Yuanqing Xia

In the realm of the cooperative control of multiagent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into GP models, promises to improve control performance by enhancing predictions during the operation. Therefore, this article investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger (CET), is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lyapunov theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.

10.1109/tnnls.2024.3446732

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Event-triggered distributed state estimation over wireless sensor networks

Automatica (2020)

Dongdong Yu Yuanqing Xia Li Li Di‐Hua Zhai

Observability

Sensor node

10.1016/j.automatica.2020.109039

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Citations (65)

Feasibility‐guaranteed safety‐critical control with high‐order control barrier function method

International Journal of Robust and Nonlinear Control (2023)

Sihua Zhang Di‐Hua Zhai Yuhan Xiong Yuanqing Xia

Abstract The optimization of control systems under the presence of safety constraints and input constraints frequently involves the decomposition into a sequence of quadratic programs (QPs) facilitated by the utilization of high‐order control barrier function (HOCBF). When the safety constraint conflicts with the input constraint, however, it leads to infeasibility within the QPs. In this article, a feasibility‐guaranteed QP is proposed to tackle the challenge posed by the conflict between HOCBF constraint and input constraint. Firstly, the classical QP is added with a feasibility constraint which is derived from input constraint and HOCBF constraint, where the parameter of feasibility constraint is updated via a new QP obtained by control sharing property. Then, Type‐2 HOCBF is investigated for the system with multiple HOCBF constraints, which effectively confines the system within a single HOCBF at the current time step. Finally, the efficacy of this approach is demonstrated through the application of obstacle avoidance in a 3‐DOF robot system.

Sequence (biology)

10.1002/rnc.7141

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