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2019 International Conference on Artificial Intelligence and computer Science, AICS 2019

作者： Cheng, Xiaoyan Dong, Yahui Wang, Hongbin Wang, Qinzhao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Weapon and Control Department Army Academy of Armored Forces Beijing100072 China

ISBN: (纸本)9781450371506

In this paper, a PD-type iterative learning control method is proposed for a class of nonlinear discrete networked control systems with measurement signal and control signal data dropouts. Proportional and differential items of the error signal are employed in this method to modify the current control signal, which takes full advantage of the historical error signals. The data dropout is described as a stochastic and independent Bernoulli process with a given probability. In addition, the zero-order holding method is introduced at the data receivers of the controller and actuator. Finally, the stability analysis and simulation results are performed to verify the convergence and effectiveness of the proposed method. © 2019 Association for Computing Machinery.

关键词： Networked control systems

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Adaptive Robust control of Multi-Machine Power Systems with control Time Delay

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Journal of Harbin Institute of Technology(New Series) 2018年第4期25卷 48-55页

作者： Zhongqiang Wu Feng Li Chunqi Du Wei Zhang Key Lab of Industrial Computer Control Engineering of Hebei Province College of Electrical EngineeringYanshan University

The adaptive H_∞ control problem of multi-machine power system in the case of disturbances and uncertain parameters is discussed,based on a Hamiltonian *** the effect of time delay during control and transmission,a Hamilton model with control time delay is ***-Krasovskii function is selected,and a controller which makes the system asymptotically stable is *** controller not only achieves the stability control for nonlinear systems with time delay,but also has the ability to suppress the external disturbances and adaptive ability to system parameter *** simulation results show the effect of the controller.

关键词： multi machine power systems time delay H∞ control uncertain parameters disturbance

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Fixed-time Stabilization control for Underactuated AUV with External Disturbance

Fixed-time Stabilization Control for Underactuated AUV with ...

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第三十八届中国控制会议

作者： Hongbin Wang Bo Su Yueling Wang Qianda Luo Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

This paper investigates the composite design of fixed-time control and fixed-time extended state observer(FTESO) for the underactuated autonomous underwater vehicle system with external disturbance. A FTESO which can estimate the disturbance and state in fixed-time unrelated to initial states of the system. Continuous fixed-time convergent control approach conjunction with FTESO are used to make sure that the system states can be convergent in a fixed time at the present of external time-varying disturbance. The horizontal plane of the AUV model is first divided into two cascade connected subsystems by use of input and state transformations, and the proposed result is further extended to the stabilization control for horizontal plane of the AUV, which considers external current disturbance. In the last, simulation results are given to demonstrate the effectiveness of the proposed method.

关键词： fixed-time extended state observer fixed-time controller convergence time AUV

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Model based reinforcement learning for robot grasping trajectory generation 4

Model based reinforcement learning for robot grasping trajec...

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4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

作者： Xue, Hongxiang Wen, Shuhuan Yang, Chao Liu, Huaping Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and System Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781728100647

Trajectory generation is a fundamental problem for successful robotic grasping. However, most of the existing work dealt with this problem using supervised learning with a prescribed model. It prevents the developed grasp strategies from being used for new unknown scenarios. In this paper, inspired by the success of the reinforcement learning method, we developed a new method which optimizes grasp trajectories using a policy search method. An important advantage of the proposed method is that the complicated 3D model could be used to generate the multi-dimensional grasp configurations. The developed algorithm has been validated on some typical robotic hand. The experimental results show that the proposed method achieves excellent performance. © 2019 IEEE.

关键词： Trajectories

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A PD-Type Iterative Learning Algorithm for Networked control Systems with Missing Data

A PD-Type Iterative Learning Algorithm for Networked Control...

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作者： Xiaoyan Cheng Hongbin Wang Qinzhao Wang Yahui Dong the Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Weapon and Control Department Army Academy of Armored Forces

In this paper,a PD-type iterative learning control method is proposed for a class of nonlinear discrete networked control systems with measurement signal and control signal data *** and differential items of the error signal are employed in this method to modify the current control signal,which takes full advantage of the historical error *** data dropout is described as a stochastic and independent Bernoulli process with a given *** addition,the zero-order holding method is introduced at the data receivers of the controller and ***,the stability analysis and simulation results are performed to verify the convergence and effectiveness of the proposed method.

关键词： siterative learning control networked control systems data dropout nonlinear system

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Dynamic event-triggered formation control for multiple quadrotor UAVs 37

Dynamic event-triggered formation control for multiple quadr...

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37th Chinese control Conference, CCC 2018

作者： Hongbin, Wang Zhen, Zhou Yueling, Wang Mingquan, Zhang Xiaojun, Xue Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9789881563941

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

关键词： Closed loop systems

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PID-type Sliding Mode control for Permanent Magnet Synchronous Motor Based on Uncertainty and Disturbance Estimator

PID-type Sliding Mode Control for Permanent Magnet Synchrono...

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Chinese Automation Congress (CAC)

作者： Jianxiong Li Songhua Wang Jie Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao China

In order to improve the speed tracking control performance of Permanent magnet synchronous motors (PMSM) with uncertainties and disturbances, an uncertainty and disturbance estimator (UDE)-based PID-type sliding mode control (SMC) approach is proposed. The proposed controller consists of two parts, the reference model, and the UDE-based sliding mode controller. The reference signals of the actual system are generated by a reference model, whose controller is designed by using a nonsingular terminal sliding mode control (NTSMC), and then employed in the UDE-based controller design. The UDE is used to estimate the uncertainties and disturbances of the system, and an UDE-based SMC algorithm is then developed. It can be proved that the resulting closed-loop system is ultimately bounded. Finally, simulation results are given to illustrate that the proposed control scheme achieves good dynamic performance and strong robustness.

关键词： Uncertainty Sliding mode control Low pass filters Steady-state Robustness Permanent magnet motors Simulation

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Improved ECO Algorithm Based on Residual Neural Network

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Journal of Physics: Conference Series 2021年第1期1732卷

作者： Huifang Qi Changmeng Wang Bo Li School of Information Science and Engineering Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao China Key Laboratory of industrial computer control engineering in Hebei Province Yanshan University Qinhuangdao China

Video target tracking is one of hot fields of computer vision, and its application is also very extensive. However, due to the complexity and variability of tracking environment, which brings some challenges to the research of target tracking. ECO(Efficient Convolution Operators) Algorithm is proposed based on convolutional neural network in three aspects. Firstly, residual neural network ResNet50 is adopted instead of convolutional neural network to extract the appearance features of target, and deeper residual neural network is applied to obtain more abundant target semantic information, so as to improve the tracking effect of tracking ***, sample space classification strategy is improved. Different weights are assigned to shallow feature and deep feature, which make the deep feature play a more important role and improve the effect of target tracking. Finally, the method of scale estimation is improved so that better bounding boxes can be estimated with the scale changing. Experimental results show that the distance accuracy and success rate of the algorithm.

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Fuzzy Neural Network algorithm based on the delay compensation force/position control structure of a redundant actuation parallel robot *

Fuzzy Neural Network algorithm based on the delay compensati...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuhuan Wen Yanfang Zha Haiyang Yu Luigi Manfredi Xiongfei Li Sen Wang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom

In this paper, a novel control method of the redundant force branch based on the force/position hybrid control structure of Smith predictor compensation is proposed. A fuzzy PI controller is designed based on Smith predictor compensation structure and it is included in the redundant force branch. This method can obtain good tracking and dynamic performance. However, fuzzy control doesn't have self-learning and adaptive ability, so fuzzy neural network (FNN) controller is used in the redundant force branch. The simulation results show that the proposed FNN algorithm based on delay compensation force/position hybrid control structure can improve the adaptability and the control accuracy of driving force of redundant branch.

关键词： Force Fuzzy control PI control Fuzzy neural networks Parallel robots Mathematical model Artificial neural networks

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