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IEEE Conference on Vehicular Technology (VTC)

作者： Shuhuan Wen F. Richard Yu Key Laboratory of Industrial Computer Control Engineering of Hebei Yanshan University Qinhuangdao China Department of Systems and Computer Engineering Carleton University Ottawa ONT Canada

Coordinated multipoint (CoMP) communication is a new method that improves energy efficiency, extends cell coverage, and increases capacity in cellular networks. However, since channel state information from the involved cells is needed in CoMP systems, practical systems that employ CoMP techniques suffer from constraints imposed by the backhaul network. In this paper, we take a novel approach to study the energy efficiency issues in cooperative wireless cellular networks with CoMP communications. Specifically, to derive the base station grouping decisions for energy-efficient transmissions in CoMP, we use stochastic predictive control algorithm. Simulation results are presented to show the effectiveness of the proposed scheme.

关键词： Base stations Wireless communication Prediction algorithms Predictive control Delay Channel state information Predictive models

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Adaptive fuzzy tracking control for robotic manipulators with adjustable gains

Adaptive fuzzy tracking control for robotic manipulators wit...

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IEEE International Conference on Mechatronics and Automation

作者： Hongbin Wang Xiaobo Wang Yueling Wang Key Laboratory of Industrial Computer Control Engineering Yanshan University슠 Qinhuangdao Hebei China College of Mechanical Engineering Yanshan University슠 Qinhuangdao Hebei China

In this paper, an adaptive fuzzy scheme for trajectory tracking is developed for robotic systems with parameter uncertainties and external disturbance. Within the scheme, a fuzzy logic system is employed to approximate the unknown robotic dynamics. Then, an adaptive fuzzy controller is obtained which combine with the adaptive methods so as to eliminate the system uncertainties and external disturbance. And the adaptive laws are obtained through stable control algorithms based on the Lyapunov stability theorem. Moreover, extra control terms are designed to cancel the approximation errors, and the adjustable gains obtained from a Fuzzy Logic System (FLS) are considered to receive the satisfying control effects. The proposed controller is robust to structured uncertainties and unstructured uncertainties, and the validity of the control scheme is shown by simulations on a novel 2-DOF parallel mechanism, which is driven by linear motors.

关键词： Programmable control Adaptive control Fuzzy control Robot control control systems Uncertainty Fuzzy systems Fuzzy logic Manipulator dynamics Trajectory

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Optimization of rolling schedules based on improved artificial fish swarm algorithm for aluminum strip hot rolling mills

Optimization of rolling schedules based on improved artifici...

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Chinese control Conference (CCC)

作者： Jing-ming Yang Shuang Xu Haijun Che Zhiwei Zhao National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Hebei Qinhuangdao 066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

The quality and yield of the products in the process of hot rolling is affected seriously by slipping phenomenon. In order to solve the phenomenon in aluminum hot rolling, on the basis of considering various factors which lead to slip, the concept of hot rolling slip factor is proposed, and a multi-objective function with equal power margin and preventing the slip phenomenon is established, and the improved artificial fish swarm algorithm is applied to optimize the reduction of first three stands. Adaptive step and improved move strategy are introduced into the proposed algorithm on the basis of basic fish swarm algorithm, which has better accuracy of optimization and faster convergence speed. The optimized schedule is applied to roll aluminum of 1050 in one hot rolling mill, and the performance is satisfied.

关键词： Marine animals Electronic mail Aluminum Optimization Schedules Strips Educational institutions

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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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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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Cooperative and active sensing in mobile sensor networks for scalar field mapping

Cooperative and active sensing in mobile sensor networks for...

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2013 IEEE International Conference on Automation Science and engineering, CASE 2013

作者： La, Hung Manh Sheng, Weihua Chen, Jiming Rutgers University Piscataway NJ 08854 United States School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States State Key Lab. of Industrial Control Technology Department of Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781479915156

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are taken and fused with the corresponding confidences using distributed consensus filters. As a result an online map of the scalar field is built with a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms. © 2013 IEEE.

关键词： Sensor nodes

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An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization Algorithm with Its Application in PID Parameter Optimization

An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Opti...

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International Symposium on computer Science and Intelligent controls (ISCSIC)

作者： Dongning Chen Xinwei Du Haowen Wang Qinggui Xian Jianhao Sha Chengyu Yao School of Mechanical Engineering Yanshan University Qinhuangdao China Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

To improve the optimization performance of the standard Aquila Optimizer (AO), an Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization (IHAOPIO) algorithm based on stochastic balancing factor and Dimension-by-dimension Centroid Opposition-Based Learning (DCOBL) strategy is proposed. Wilcoxon test is introduced to statistically analyze the data of six classical benchmark functions. Compared with several other optimization algorithms, the proposed IHAOPIO algorithm has better optimization capability in accuracy. The improved algorithm is used to the PID parameter optimization of one electro-hydraulic position servo system and achieves good control effect.

关键词： computer science Accuracy Benchmark testing Servomotors Optimization Standards Intelligent control

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An improved model predictive control method for wave energy converters

An improved model predictive control method for wave energy ...

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第40届中国控制会议

作者： Zhenchun Wang Feng Luan Nianguo Wang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Engineering Research Centre of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

The model accuracy and objective function complexity are key issues that remain in the realisation of model predictive control（MPC） of wave energy converters（WECs）.In this study,the viscous force is considered in a WEC model to improve model accuracy,and an improved model predictive control（IMPC） method is proposed that can reduce the quadratic programming（QP） performance requirements of the processor while maintaining a high energy conversion *** effectiveness of the IMPC method was verified through numerical simulations.

关键词： Viscous force Wave energy converters Model predictive control

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Multi-channel assignment in wireless sensor networks: A game theoretic approach

Multi-channel assignment in wireless sensor networks: A game...

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IEEE INFOCOM 2010

作者： Yu, Qing Chen, Jiming Fan, Yanfei Shen, Xuemin Sun, Youxian State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou 310027 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424458363

In this paper, we formulate multi-channel assignment in Wireless Sensor Networks (WSNs) as an optimization problem and show it is NP-hard. We then propose a distributed Game Based Channel Assignment algorithm (GBCA) to solve the problem. GBCA takes into account both the network topology information and transmission routing information. We prove that there exists at least one Nash Equilibrium in the channel assignment game. Furthermore, we analyze the sub-optimality of Nash Equilibrium and the convergence of the Best Response in the game. Simulation results are given to demonstrate that GBCA can reduce interference significantly and achieve satisfactory network performance in terms of delivery ratio, throughput, channel access delay and energy consumption. ©2010 IEEE.

关键词： Wireless sensor networks

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm

Trajectory Optimization of Manipulator for Loading and Unloa...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Dongliang Zhao Yiming Fang Chen Yang Jingliang Xin Key Lab of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao Hebei Province China Hebei Iron and Steel Group Tangshan Hebei Province China

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter.

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