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IEEE/CAA Journal of Automatica Sinica 2015年第2期2卷 134-142页

作者： Yuanqing Xia School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

The concept of cloud control systems is discussed in this paper, which is an extension of networked control systems (NCSs). With the development of internet of things (IOT), the technology of NCSs has played a key role in IOT. At the same time, cloud computing is developed rapidly, which provides a perfect platform for big data processing, controller design and performance assessment. The research on cloud control systems will give new contribution to the control theory and applications in the near future. © 2014 Chinese Association of Automation.

关键词： Big data Cloud computing Computation theory control systems control theory controllers Data handling Distributed computer systems Embedded systems Internet of things

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On Event-triggered Adaptive Robust control 36

On Event-triggered Adaptive Robust Control

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第36届中国控制会议

作者： Yuan Huang Junzheng Wang Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (纸本)9781538629185

In this work,an event-triggered adaptive robust controller（ET-ARC） design is considered for a continuous-time nonlinear system combined with structural uncertainties and parameter *** the event-trigger scheme,the controller only can obtain the sampled date of the output measurement at some certain instants determined by the designed triggering condition and therefore the performance of the ET-ARC depends on the triggering condition ***,in this work,for the adaptive robust control scheme,an event-triggered transmission strategy is proposed,which relies on the output measurement of the nonlinear ***,based on this strategy,the design idea of the adaptive robust controller is presented such that the tracking performance is ***,based on a more general assumption,we provide a possible formation of control signal which satisfies the requirement proposed.

关键词： Adaptive robust control event-triggered transmission parameter estimation

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Multi-Objective Cooperative Salvo Attack Against Group Target

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Journal of Systems Science & complexity 2018年第1期31卷 244-261页

作者： ZENG Jie DOU Lihua XIN Bin School of Automation State Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

This paper proposes a solution for the problem of cooperative salvo attack of multiple cruise missiles against targets in a group. Synchronization of the arrival time of missiles to hit their common target, minimizing the time consumption of attack and maximizing the expected damage to group targets are taken into consideration simultaneously. These operational objectives result in a hierarchical mixed-variable optimization problem which includes two types of subproblems, namely the multi-objective missile-target assignment（MOMTA） problem at the upper level and the time-optimal coordinated path planning（TOCPP） problems at the lower level. In order to solve the challenging problem, a recently proposed coordinated path planning method is employed to solve the TOCPP problems to achieve the soonest salvo attack against each target. With the aim of finding a more competent solver for MOMTA, three state-of-the-art multi-objective optimization methods（MOMs）,namely NSGA-II, MOEA/D and DMOEA-εC, are adopted. Finally, a typical example is used to demonstrate the advantage of the proposed method. A simple rule-based method is also employed for comparison. Comparative results show that DMOEA-εC is the best choice among the three MOMs for solving the MOMTA problem. The combination of DMOEA-εC for MOMTA and the coordinated path planning method for TOCPP can generate obviously better salvo attack schemes than the rule-based method.

关键词： Coordinated path planning missile-target assignment multi-missile salvo attack multiobjective optimization

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Event-triggered attitude tracking for rigid spacecraft

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Science China(Information Sciences) 2019年第12期62卷 178-193页

作者： Deheng CAI Hengguang ZOU Junzheng WANG Yuan HUANG Dawei SHI State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology China Academy of Space Technology

This study aims to investigate the problem of attitude control for a spacecraft with inertial uncertainties, external disturbances, and communication restrictions. An event-triggered active disturbance rejection control approach is proposed for attitude tracking of the spacecraft. An event-triggered mechanism is introduced together with an extended state observer to jointly monitor the system states and total disturbances. The observation error is proved to be uniformly bounded. Based on the proposed control scheme,the integrated tracking system is shown to be asymptotically stable, implying successful attitude tracking of the spacecraft for the desired motion. Numerical results illustrate the effectiveness of the control strategy in achieving satisfactory tracking performance with a reduced data-transmission cost.

关键词： spacecraft attitude tracking active disturbance rejection control event-triggered control extended state observer

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Deep reinforcement learning for optimal denial-of-service attacks scheduling

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Science China(Information Sciences) 2022年第6期65卷 98-106页

作者： Fangyuan HOU Jian SUN Qiuling YANG Zhonghua PANG State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology

We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.

关键词： optimal denial-of-service attack scheduling optimization limited energy deep reinforcement learning

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Model and Simulation of Multipath Error in DLL for GPS Receiver

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Chinese Journal of Electronics 2014年第3期23卷 508-515页

作者： CHENG Lan CHEN Jie XIE Gang Taiyuan University of Technology Education Ministry Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

Although different multipath error models of Delay lock loop(DLL) used in GPS receiver are established, they have never been put together for comparison. Furthermore, no universal simulation method is developed to get a fair comparison among these models. A new model with implicate expression is hence proposed for the coherent DLL and the noncoherent Dot-product(DOT) power mode DLL. Meanwhile, a new simulation method based on the anonymous function in Matlab, which is especially suitable for models with implicit expression,is also proposed to compare the new model with the existing ones. The theoretical analysis and simulation results show that the existing models are the special case of the proposed one. The new simulation method can be used for the comparison of different multipath error models and the multipath error analysis of other DLLs for which only the implicit model is available.

关键词： Delay lock loop(DLL) Multipath Multipath error model Positioning system

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Adaptive robust control for four-motor driving servo system with uncertain nonlinearities

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control Theory and Technology 2017年第1期15卷 45-57页

作者： Wei ZHAO Xuemei REN Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China

A novel adaptive robust control （ARC） is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.

关键词： Four-motor driving servo systems adaptive robust control velocity synchronization control friction backlash,actuation failures

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Deep Multimodal Learning and Fusion Based intelligent Fault Diagnosis Approach

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Journal of Beijing Institute of Technology 2021年第2期30卷 172-185页

作者： Huifang Li Jianghang Huang Jingwei Huang Senchun Chai Leilei Zhao Yuanqing Xia Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of TechnologyBeijing 100081China

Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge *** data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT *** how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a *** this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices ***,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal ***,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential ***,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis *** experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.

关键词： fault diagnosis deep learning multimodal heterogeneous data multimodal fused features

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Stability Analysis of Networked control Systems with Aperiodic Sampling and Time-Varying Delay

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IEEE Transactions on Cybernetics 2017年第8期47卷 2312-2320页

作者： Chen, Jie Meng, Su Sun, Jian Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China

This paper addresses the stability of networked control systems with aperiodic sampling and time-varying network-induced delay. The sampling intervals are assumed to vary within a known interval. The transmission delay is assumed to belong to a given interval. The closed-loop system is first converted to a discrete-time system with multiple time-varying delays and norm-bounded uncertainties resulting from the variation of the sampling intervals. And then, it is transformed into a delay-free system being form of an interconnection of two subsystems. By utilizing scaled small gain theorem, an asymptotic stability criterion for the closed-loop system is proposed in terms of linear matrix inequality. Finally, numerical examples demonstrate the effectiveness of the proposed method and its advantages over existing methods. © 2013 IEEE.

关键词： Networked control systems

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Optimal Switching Integrity Attacks on Sensors in Industrial control Systems

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Journal of Systems Science and complexity 2019年第5期32卷 1290-1305页

作者： Wu, Guangyu Sun, Jian The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China

In this article, an optimal switching integrity attack problem is investigated to study the response of feedback control systems under attack. The authors model the malicious attacks on sensors as additive norm bounded signals. The authors consider an attacker who is only capable of launching attacks to limited number of sensors once a time and changing the combinations of attacked sensors all over the time. The objective of this paper is to find the optimal switching sequence of these combinations and the optimal attack input. The authors solve this problem by transforming it into a traditional optimal control problem with new control variables vary continuously in the range [0, 1]. The optimal solutions of the new control variables are of bang-bang-type. Therefore, an algebraic switching condition and an optimal attack input can be obtained. Finally, numerical results are provided to illustrate the effectiveness of the methods. © 2019, The Editorial Office of JSSC & Springer-Verlag GmbH Germany.

关键词： Adaptive control systems

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