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第37届中国控制会议

作者： Shuhuan Wen Jianhua Chen Zhen Li Ahmad B.Rad Kamal Mohammed Othman the Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University the school of Engineering Sciences Simon Fraser University

This paper proposes a fuzzy Q-learning(FQL)algorithm to solve the problem of the robot obstacle avoidance in unknown *** algorithm is used to localize the position of the *** Q-learning algorithm,optimized Q-learning algorithm,FQL algorithm are *** simulation results show that FQL algorithm has a faster learning speed than other two algorithms and the results demonstrate that the fuzzy Q-learning obstacle avoidance algorithm is effective.

关键词： FASTSLAM fuzzy logic control path planning obstacle avoidance Q-learning

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Research and Application of Flatness control for Tandem Cold Rolling

Research and Application of Flatness Control for Tandem Cold...

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第37届中国控制会议

作者： LI Xiaogang FANG Yiming LIU Le Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling

Compound wave defect occurs in flatness during tandem cold rolling when high-end automobile and household appliance plate are produced, which has several impact on line speed and quality of final product. In this paper, a further research is done on sourcing cause of formation for the flatness defect. In combination with adjusting actuators and automatic flatness control principles, Achieving target flatness by rolls tilting, work rolls bending, middle rolls bending, and section cooling *** control output of tilting, work rolls and middle rolls bending are calculated by the least square method. Section cooling control output of rolling is calculated by the typical fuzzy control theory. The least square method and fuzzy control algorithm are optimized so as to enhance control accuracy of automatic flatness, and at the same time largely improve flatness of steel strip, reduce working strength of operators and improve production efficiency of production line.

关键词： Tandem cold rolling Automatic flatness control Least square method Fuzzy control

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Fuzzy Q-learning obstacle avoidance algorithm of humanoid robot in unknown environment

Fuzzy Q-learning obstacle avoidance algorithm of humanoid ro...

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Chinese control Conference

作者： Shuhuan Wen Jianhua Chen Zhen Li Ahmad B. Rad Kamal Mohammed Othman Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Engineering Sciences Simon Fraser University Surrey BC Canada

This paper proposes a fuzzy Q-learning (FQL) algorithm to solve the problem of the robot obstacle avoidance in unknown environment. FastSLAM algorithm is used to localize the position of the robot. Traditional Q-learning algorithm, optimized Q-learning algorithm, FQL algorithm are compared. The simulation results show that FQL algorithm has a faster learning speed than other two algorithms and the results demonstrate that the fuzzy Q-learning obstacle avoidance algorithm is effective.

关键词： Collision avoidance Path planning Convergence Humanoid robots Mobile robots Fuzzy sets

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An Ameliorated Moth-flame Optimization Algorithm

An Ameliorated Moth-flame Optimization Algorithm

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第37届中国控制会议

作者： Xiao-dong Zhao Yi-ming Fang Zhuang Ma Miao Xu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Tangshan University

To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The Moth-Flame Optimization（MFO） algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO,an ameliorated Moth-flame optimization algorithm（A-MFO） that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.

关键词： Moth-flame optimization algorithm crisscross optimization algorithm stochastic optimization convergence

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Electrochemical Detection of Insulin Based on Screen Printed Electrode Modified by Nickel Hydroxide

Electrochemical Detection of Insulin Based on Screen Printed...

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International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS)

作者： Hongyu Zhang Zhikun Zhan Yuliang Zhao Xiaopeng Sha Xiaodong Yu Hui Sun Lijia Gu Jiali Liu Lianqing Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State key Laboratory of Robotics Chinese Academy of Sciences

ISBN: (纸本)9781538694091;9781538694084

This paper reports a novel electrochemical method for the detection of insulin in ultra-pure water. A screen printed electrode (SPE) modified by Nickel hydroxide was used as the working electrode, one with good activity of electro-catalytic oxidation-reduction for insulin. The SPE was modified by Nickel hydroxide using the electrodeposition method. The results showed that Nickel hydroxide was homogeneously electrodeposited on the surface of the working electrode. A coin-size electrolytic tank was fabricated with PDMS, which only requires 0.3ml of sample for the detection. The concentration of insulin was analyzed using the methods of Cyclic voltammetry (CV), Electrochemical impedance spectroscopy (EIS) and Chronoamperometry (i-t). The Nickel hydroxide used for detecting insulin showed good analytical characteristics, such as a high sensitivity of 14.797μA·μM -1 , a good linear detection range of 100nM-5μM, and a low detection limit of 93nM. This nickel hydroxide modified SPE electrode has a much smaller size and is easier to fabricate than traditional electrodes, making it a much more promising candidate for an insulin sensor.

关键词： Electrodes Insulin Nickel Oxidation Sensitivity Surface treatment Diabetes

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Looseness Detection of Rail Fasteners Using MEMS Sensor and Power Spectrum Entropy

Looseness Detection of Rail Fasteners Using MEMS Sensor and ...

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International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS)

作者： Hao Sun Zhikun Zhan Hongyu Zhang Junshan Liu Xiaopeng Sha Xiaodong Yu Jianing Yu Yuliang Zhao Lianqing Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State key Laboratory of Robotics Chinese Academy of Sciences

ISBN: (纸本)9781538694091;9781538694084

Rail fasteners, a key component of a railway track, are used to connect the rail and the sub-rail foundation together with the required degree of elasticity. Severely loose or failed fasteners may lead to serious safety hazards for rail transportation. However, current strategies for determining fastener looseness are mainly implemented manually, which is labor-intensive, inefficient and less-than-objective. In this paper, we propose a system that uses a MEMS acceleration meter and a wireless data transmission technique to detect railway fasteners. Vibration was generated by hitting the rail with a hammer. Then the acceleration data was transmitted to the cloud by the Global System for Mobile Communications (GSM) technique. The fastener looseness was analyzed using power spectrum entropy and frequency domain analysis. Results from the field experiments show that our method can identify the fastener's loosening state accurately and automatically. This wireless, low-cost fastener inspection system is expected to provide an effective approach to improving the efficiency of regular railway maintenance..

关键词： Conferences Artificial intelligence Medical services Robot sensing systems IP networks

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Path Planning of Humanoid Arm Based on Deep Deterministic Policy Gradient

Path Planning of Humanoid Arm Based on Deep Deterministic Po...

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IEEE International Conference on Robotics and Biomimetics

作者： Shuhuan Wen Jianhua Chen Shen Wang Hong Zhang Xueheng Hu department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton Canada

ISBN: (纸本)9781728103785;9781728103778

The robot arm with multiple degrees of freedom and working in a 3D space needs to avoid obstacles during the grasping process by its end effector. Path planning to avoid obstacles is very important for accomplishing a grasping task. This paper proposes a new obstacle avoidance algorithm, based on an existing deep reinforcement learning framework called deep deterministic policy gradient (DDPG). Specifically, we propose to use DDPG to plan the trajectory of a robot arm to realize obstacle avoidance. The rewards are designed to overcome the difficulty in convergence of multiple rewards, especially when the rewards are antagonistic with respect to each other. Obstacle avoidance of the robot arm using DDPG is achieved by self-learning, and the convergence problem caused by the high dimension state input and multiple return values is solved. The simulation model of an arm of the Nao robot is built based on the MuJoCo simulation environment. The simulation demonstrates that the proposed algorithm successfully allows the robot arm to avoid obstacles.

关键词： Manipulators Neural networks Robot kinematics Planning Reinforcement learning Trajectory

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Open-closed-loop iterative learning control for a class of nonlinear systems with random data dropouts

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IOP Conference Series: Materials Science and engineering 2018年第1期359卷

作者： X Y Cheng H B Wang Y L Jia YH Dong The Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

In this paper, an open-closed-loop iterative learning control (ILC) algorithm is constructed for a class of nonlinear systems subjecting to random data dropouts. The ILC algorithm is implemented by a networked control system (NCS), where only the off-line data is transmitted by network while the real-time data is delivered in the point-to-point way. Thus, there are two controllers rather than one in the control system, which makes better use of the saved and current information and thereby improves the performance achieved by open-loop control alone. During the transfer of off-line data between the nonlinear plant and the remote controller data dropout occurs randomly and the data dropout rate is modeled as a binary Bernoulli random variable. Both measurement and control data dropouts are taken into consideration simultaneously. The convergence criterion is derived based on rigorous analysis. Finally, the simulation results verify the effectiveness of the proposed method.

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Adaptive finite time consensus control of multiple mechanical systems under a switching graph 35

Adaptive finite time consensus control of multiple mechanica...

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第35届中国控制会议

作者： WANG Hongbin ZHOU Zhen WANG Yueling Key Lab of Industrial Computer Control Engineering of Hebei Province Yansnan University

ISBN: (纸本)9781509009107

In this paper,adaptive finite time consensus control problem is investigated for multiple mechanical systems with *** adaptive distributed control algorithms are proposed for the multiple mechanical systems under a switching undirected graph,which is addressed through a novel Markov *** any detailed information of the system model,the adaptive distributed controller has a faster and higher precision tracking *** is shown that the states of the mechanical systems can reach a consensus within finite *** finally,an illustrative simulation on the multiple robotic manipulators is given to demonstrate the effectiveness of the proposed controller.

关键词： Finite time consensus Adaptive control Multiple mechanical system Markov chain

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Friction compensation control of space manipulator considering the effects of gravity 35

Friction compensation control of space manipulator consideri...

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第35届中国控制会议

作者： QIN Li JIA Xiao-jing LIU Cai-feng LIU Fu-cai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781509009107

All space manipulators are debugged on the ground and on-orbit serviced in space. Compared with in ground gravity environment their friction characteristics in space microgravity environment have great changes. For sake of tracking the desired trajectory, in both ground and space, considering these characteristics, a friction compensation control method based on an Active Disturbance Rejection control(ADRC) is proposed and its effectiveness is validated on a planar 2-DOF free-floating space manipulator. The friction models and the dynamic models under different gravity environment have been derived based on the torque balance, the Kane equation and the Lagrange method. Particle Swarm Optimization(PSO) is put forward to optimize parameters of ADRC so as to improve the efficiency of designing process. Compared with PD controller in tracking simulation under different gravity environment, the results verify the advantage of the proposed improved control method.

关键词： space manipulator gravity friction compensation ADRC Trajectory tracking PSO

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