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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuhuan Wen Yang Liu Leibo Zheng Fuchun Sun Bin Fang Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Dept. of Computer Science and Technology Tsinghua University Beijing China

This paper proposes a system, by which humanoid robots could imitate human movements to learn human behaviors. Considering the security problem during humanoid robot imitation process, a method of stability identification based on improved Multi-Layer Perception (MLP) is proposed. Firstly, the positions of the human joints are collected by a Kinect. Secondly, the human joints information is transformed into robot angles information by the inverse kinematics equation. Finally, the stability of the robot is identified by the deep neural network, which has the robot angles as its input. Simulations and experimental results demonstrate that compared with the Back Propagation neural network (BPnn), RBF neural network (RBFnn), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), MLP neural network has the best identification accuracy. And the filters we used can overcome the problem of speed pulse, to some extent, and achieved an ideal imitation result.

关键词： Neural networks Humanoid robots Robot kinematics Stability analysis Torso Shoulder

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Research and application of flatness control for tandem cold rolling 37

Research and application of flatness control for tandem cold...

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37th Chinese control Conference, CCC 2018

作者： Xiaogang, Li Yiming, Fang Le, Liu Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Qinhuangdao066004 China

ISBN: (纸本)9789881563941

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. Adjustment 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. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Fuzzy control

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Adaptive Dual Model Differential Evolution Algorithm Based on Cloud Model

Adaptive Dual Model Differential Evolution Algorithm Based o...

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第30届中国控制与决策会议

作者： Hu Zhongquan Zhou Zhen Wang Hongbin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

To solve the problem of premature convergence and search stagnation in traditional differential algorithm, this paper presents an adaptive dual model differential evolution algorithm based on cloud model(ADDEC). The variation model of ADDEC is produced by combining basic variation with cloud variation. And the adaptive operators are referenced respectively for scale factor and interactive probability during the operation of variation and crossover. The robustness and the convergence rate of the algorithm are enhanced. Moreover the relationship between global search and local search of the algorithm is improved. The diversity of the population is guaranteed at the same time. It is tested by typical high dimension benchmark function and compared with other algorithms, and the results show that the ADDEC has better precision, high convergence speed and makes it easy to jump out the local optimal solution.

关键词： Differential evolution algorithm Cloud model Evolution strategy Adaptive

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Dynamic Event-Triggered Formation control for Multiple Quadrotor UAVs

Dynamic Event-Triggered Formation Control for Multiple Quadr...

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第37届中国控制会议

作者： WANG Hongbin ZHOU Zhen WANG Yueling ZHANG Mingquan XUE Xiaojun Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

This paper deals with the formation control problem for a group of quadrotor unmanned aerial vehicles（UAVs） through a modified dynamic event-triggered control protocol. The stability proof for the state space origin of the overall closed-loop system is derived from the singular perturbation method and Lyapunov stability theory. An event-triggered formation control protocol in terms of a dynamically varying threshold parameter is delicately carried out while acquiring satisfactory resource efficiency, and Zeno behavior of triggering time sequences is excluded. Finally, simulations on four quadrotor UAVs are given to verify the effectiveness of the theoretical results.

关键词： Formation control Singular perturbation Dynamic event-triggered controller Multiple quadrotor UAVs

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Fuzzy Q-learning obstacle avoidance algorithm of humanoid robot in unknown environment 37

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

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37th Chinese control Conference, CCC 2018

作者： Wen, Shuhuan Chen, Jianhua Li, Zhen Rad, Ahmad B. Mohammed Othman, Kamal Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China School of Engineering Sciences Simon Fraser University SurreyBCV3T 0A3 Canada

ISBN: (纸本)9789881563941

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. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Anthropomorphic robots

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A Learning Evasive Email-Based P2P-Like Botnet

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China Communications 2018年第2期15卷 15-24页

作者： Zhi Wang Meilin Qin Mengqi Chen Chunfu Jia Yong Ma College of Computer and Control Engineering Nankai University Tianjin 300350 China Information Security Evaluation Center of Civil Aviation Civil Aviation University of China Tianjin 300300 China Key Lab. on High Trusted Information System in Hebei Province Baoding 071002 China

Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.

关键词： malware botnet learning evasion command and control

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An Ameliorated Moth-flame Optimization Algorithm

An Ameliorated Moth-flame Optimization Algorithm

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Chinese control Conference

作者： Xiao-dong Zhao Yi-ming Fang Zhuang Ma Miao Xu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

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： Optimization Fires Convergence Sociology Statistics Benchmark testing Heuristic algorithms

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Sensor Fault Detection of Continuous Casting Mold Vibration System Driven by Servo Motor

Sensor Fault Detection of Continuous Casting Mold Vibration ...

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Chinese control Conference (CCC)

作者： Xia Wu Jianxiong Li Yiming Fang Zhiqiang Wang Key Lab of Industrial Computer Control Engineering of Yanshan University Qinhuangdao Hebei Province China

The problem of sensor fault diagnosis and reconstruction of the continuous casting mold vibration displacement system driven by servo motor is investigated in this paper, and a fault reconstruction method based on adaptive sliding mode observer is proposed. Firstly, the matching condition of the observer is satisfied by constructing an auxiliary output, and a new state variable is selected as the low-pass filter with the fault vector output. Then an adaptive sliding mode observer is designed for the auxiliary output system in the present of the unknown upper bound of the fault, in which the adaptive switching gain is updated online. The gain matrix of the observer is obtained by solving a LMI optimization problem. And a linear transforming matrix is introduced to realize the direct estimation of the fault by the principle of the equivalent control output error injection. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

关键词： Observers Casting Mathematical model Servomotors Adaptive systems Vibrations Upper bound

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A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy 36

A novel multi-objective decomposition particle swarm optimiz...

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第36届中国控制会议

作者： Lixin Wei Rui Fan Xin Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781538629185

Comprehensive learning particle swarm optimization（CLPSO） algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of selective solution mechanism. In this article, a multi-objective decomposition particle swarm optimization based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy for multi-objective problems to prevent premature convergence;updates the leading particles by decomposition method to enhance the distribution of solutions;adds the archive to preserve non-dominated solutions, and adopts mutation in archive to avoid falling into local optimum. The proposed approach is compared with three multi-objective evolutionary algorithms and the results indicate that the proposed approach is competitive respect to which it is compared in most of the test problems adopted.

关键词： comprehensive learning particle swarm optimization multi-objective optimization decomposition

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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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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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