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第三十三届中国控制会议

作者： GAN Yahui DAI Xianzhong DA Qi School of Automation Southeast University Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781479947249

The problem of how to emulate a manual welding process by two cooperative robotic manipulators is investigated in this *** emulating process is intrinsically a trajectory planning process for the two cooperative *** of skilled welders and technical requirements in a welding process have been modeled and integrated into the robot trajectory planning *** model of trajectory planing for two cooperative robots doing a welding task is in a constrained optimization form.A Genetic Algorithm solver has been adopted to solve such an optimization ***-connect-pipe welding experiment has been carried out at the end of the paper and the weld results verified the effectiveness of our method.

关键词： emulate manual welding,cooperative robot,Genetic Algorithm,Pipe-connect-pipe welding

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AST: Annulus Swin Transformer For Pedestrians Detection Under Top-view Fisheye Image 12

AST: Annulus Swin Transformer For Pedestrians Detection Unde...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Lu, Cheng Wei, Xuan Lu, Xiaobo School of Software Engineering Southeast University Suzhou China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Suzhou China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9798400707988

Fisheye images have a larger field of view than ordinary perspective cameras and can obtain more scene information. Therefore, object detection based on top-view fisheye images has wide applications in security monitoring, passenger flow perception, and industrial detection fields. However, due to the radial distortion symmetry of fisheye images, most current networks still need to adapt to this radial distortion symmetry. Therefore, we proposed the Annulus Swin Transformer(AST) for top-view fisheye images, which includes Annulus Window (AW) and Annulus Position Coding (APC). Traditional rectangular windows can break the radial distortion symmetry in top-view fisheye images, instead of taking advantage of it to improve the network's perception of distortion. Annulus-Window transforms it into an inner-to-outer annulus window to calculate self-attention, thereby maintaining the distortion symmetry of the fisheye image within each window, allowing the network to perceive better the degree of distortion in the fisheye image and get better detection performance. Due to the varying degree of distortion of images within each annulus, APC introduces an annulus position to work with Annulus window based on the original position encoding, enabling it to contain annulus position information. The experimental results on the dataset SEU-fisheye show that our method improves the neural network's performance markedly. © 2023 ACM.

关键词： Image enhancement

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A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

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Science China(Information Sciences) 2015年第12期58卷 147-161页

作者： WEI QingLai LIU DeRong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q learning policy iteration neural networks nonlinear systems optimal control

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Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof

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Chinese Physics B 2013年第9期22卷 305-311页

作者：宋睿卓肖文栋孙长银魏庆来 School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.

关键词： chaotic systems approximation error adaptive dynamic programming optimal tracking control

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A Neural-Network-Based controller for Piezoelectric-Actuated Stick-Slip Devices

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2598-2607页

作者： Cheng, Long Liu, Weichuan Yang, Chenguang Huang, Tingwen Hou, Zeng-Guang Tan, Min State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Zienkiewicz Centre for Computational Engineering Swansea University Swansea U.K. Texas A&M University at Qatar Doha Qatar State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the endeffector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.

关键词： End-effector neural network piezoelectric actuator (PEA) stick-slip

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Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

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Chinese Physics B 2017年第3期26卷 268-275页

作者：宋睿卓魏庆来 School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration （PI） is introduced to solve the rain-max optimization problem. The off-policy adaptive dynamic programming （ADP） algorithm is then proposed to find the solution of the tracking Hamilton-Jacobi- Isaacs （HJI） equation online only using measured data and without any knowledge about the system dynamics. Critic neural network （CNN）, action neural network （ANN）, and disturbance neural network （DNN） are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded （UUB） of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem.

关键词： adaptive dynamic programming approximate dynamic programming chaotic system zero-sum

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Effective Indoor Fire Detection with Channel Shuffle Module 21

Effective Indoor Fire Detection with Channel Shuffle Module

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Ge, Haotian Cao, Yichao Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

In recent years, methods based on computer vision and deep learning become the mainstream approaches in fire detection. However, the expensive computation cost of 3D convolutional neutral network (CNN) is unbearable and it is difficult for them to capture the fire regions of videos in time. In this paper, we design a module named channel shuffle module (CSM) based on 2D CNN to keep the balance between computation cost and accuracy. By fusing RGB frame and differential frame, CSM improves the ability of 2D CNN in temporal information extraction which much less cost than methods based on 3D CNN. Four different structures of CSM are proposed and we choose the best one by experiment results. Also, experiments prove that the performances of TSN and TSM are improved with CSM in sequence classification. The accuracy of TSM with CSM is 99.2045%, false positive rate reaches 0.7890% and false negative rate reaches 0.4530%, which demonstrates the efficiency of CSM in temporal feature modeling.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Fires

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A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

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Chinese Physics B 2014年第5期23卷 138-144页

作者：宋睿卓肖文栋魏庆来 School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

We develop an online adaptive dynamic programming （ADP） based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

关键词： adaptive dynamic programming adaptive critic designs optimal control continuous-time chaoticsystem

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Task Sequence Planning Algorithm for Cooperative Assembly of Dual-arm Robot

Task Sequence Planning Algorithm for Cooperative Assembly of...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Jin Gan, Yahui Wang, Zhengwei Dai, Xianzhong School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665426473

Dual-arm collaborative robots have gradually become a research hotspot in the field of robotics due to their unique collaboration and flexibility, and they have a wide range of application prospects especially in the assembly manufacturing industry. However, the existing dual-arm robot lacks autonomy in assembly operations, especially when faced with a large number of randomly placed assembly parts, how to make the robot have independent assembly task decision-making ability is a challenge. In response to this problem, this paper proposes a new dual-arm robot collaborative assembly task sequence planning algorithm, which fully considers the constraints of the dual-arm robot's mechanical structure, kinematics, characteristics of objects to be assembled, interference between objects, etc.. Firstly, The algorithm uses the Monte Carlo method to analyze the working space of the left and right arms of the dual-arm robot, while taking into account the structural constraints of the robot. Then, according to the characteristics of the object to be assembled, the idea of interference sphere is proposed to analyze the interference between objects in the assembly task. The influence of the operation, and finally combined with the movement cost of the dual-arm assembly, the assembly task sequence is effectively planned. The algorithm was verified by simulation using the ABB YuMi dual-arm collaborative robot, and the experimental results proved the feasibility and effectiveness of the algorithm. © 2021 IEEE

关键词： Monte Carlo methods

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A Two-step Model for Multi-object Tracking 21

A Two-step Model for Multi-object Tracking

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Zhang, Shuaishuai Lu, Xiaobo Du, Songlin Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education School of Automation Nanjing China

ISBN: (纸本)9781450389853

Multi-object tracking is widely used in video analysis. However, due to the limitation of detector performance, many multi-object tracking models have the problem of detecting two objects into one object in some occlusion scenes. In this paper, we propose a twostep model for handling this problem. In the first step model, the non-occlusion targets are detected and embeddings are extracted, while the occlusion areas are identified. The second step model processes the occlusion areas to obtain occlusion targets' accurate positions and embeddings. Finally, we integrate and optimize the output results of the two steps models. Experiments show that the number of false positives and missed positives in our model's object detection is significantly reduced. The multi-object tracking performance (MOTA metric) is improved by nearly 3% compared with other models.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Embeddings

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