周东华
动态系统的故障诊断与预报,容错控制,智能维护技术,机动目标跟踪与定位等。
个性化签名
- 姓名:周东华
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师, “973”、“863”首席科学家, 优秀教师/优秀教育工作者, 教育部“新世纪优秀人才支持计划”入选者, 国家杰出青年科学基金获得者
- 职称:-
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学科领域:
控制理论
- 研究兴趣:动态系统的故障诊断与预报,容错控制,智能维护技术,机动目标跟踪与定位等。
周东华:男,生于1963年9月, 河北邯郸市人。 分别于1985,1988,1990年在上海交通大学获学士、硕士、博士学位。1991年至1992年在浙江大学工业控制技术研究所做博士后,1993年1月至1994年9月在北京理工大学自动控制系任副研究员。1994年9月至1996年10月为德国杜伊斯堡大学洪堡学者。1996年12月加入清华大学, 1997年7月晋升教授。目前为清华大学过程控制工程研究所所长,清华大学控制理论与控制工程学科责任教授,博士生导师。其中,2001年7月至2002年1月,为美国耶鲁大学访问教授。主要研究方向是:动态系统的故障诊断与预报,容错控制,智能维护技术,机动目标跟踪与定位等。已在国际学术刊物上发表论文40余篇、出版学术专著3部、教材1本,主编全国性学术会议论文集1本。所发表的论文(著作)已被国内外其他学者的600余篇论文(著作)引用1600余次(处)。目前的主要学术兼职有:国际自动控制联合会(IFAC)技术过程的故障诊断与安全性技术委员会委员(中国的唯一委员), IEEE 高级会员, 中国自动化学会理事、副秘书长,中国自动化学会技术过程的故障诊断与安全性专业委员会副主任兼秘书长, 中国自动化学会青年工作委员会副主任, 《自动化学报》,《信息与控制》,《控制工程》等杂志编委。
已主持国家973子项目, 863, 国家自然科学基金等国家和省部级科研项目17项。 1995年获国家教委科技进步二等奖; 1997年获国家教委资助优秀青年教师基金; 1998年荣获了第六届中国青年科技奖、“国氏”博士后奖励基金 、清华大学学术新人奖(为清华大学青年教授最高学术奖);1999年获北京市科技进步三等奖、 入选 “教育部跨世纪优秀人才” 培养计划, 并获霍英东教育基金会高等院校(研究类)青年教师奖; 2000年获国防科学技术奖三等奖, 并荣获国家杰出青年科学基金(总理基金); 2001年获国家优秀科技图书奖三等奖;2005年获全军科技进步二等奖,并获全国优秀博士后称号。
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主页访问
2875
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成果阅读
771
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成果数
11
【期刊论文】The modeling and estimation of asynchronous multirate multisensor dynamic systems
周东华, L.P. Yana, B.S. Liua, b, D.H. Zhoua, *
Aerospace Science and Technology 10(2006)63-71,-0001,():
-1年11月30日
An asynchronous data fusion problem based on a kind of multirate multisensor dynamic system is studied. The system is observed by multirate sensors independently, with the state model known at the finest scale. Under the assumption that the sampling rates of sensors decrease successively by any positive integers, the discrete dynamic system models are established based on each single sensor and an asynchronous multirate multisensor state fusion estimation algorithm is presented. Theoretically, the estimate is proven to be unbiased and the optimal in the sense of linear minimum covariance, the fused estimate is better than the Kalman filtering results based on each single sensor, and the accuracy of the fused estimate will decrease if any of the sensors' information is neglected. The feasibility and effectiveness of the algorithm are shown through simulations.
Asynchronous, Multirate, Multisensor, Kalman filter, Data fusion
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76浏览
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435下载
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引用
周东华, X.Q. Xie, D.H. Zhou, Y.H. Jin*
Journal of Process Control 9(1999)337-350,-0001,():
-1年11月30日
Generic Model Control (GMC) is a control algorithm capable of using nonlinear process model directly. Parameters in GMC controllers are easily tuned, and measurable disturbances can be compensated e
Generic model control, Strong tracking
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372下载
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【期刊论文】Fault detection and identification for uncertain linear time-delay systems
周东华, Canghua Jiang, D.H. Zhou*
Computers and Chemical Engineering 30(2005)228-242,-0001,():
-1年11月30日
In this paper, a robust fault detection and identification approach based on an adaptive observer is developed for uncertain continuous linear time-invariant systems with multiple discrete time-delays in both states and outputs. State and output faults of bias type that may evolve slowly or abruptly are considered, and the delay system is disturbed by unstructured bounded unknown inputs. Based on the scheme of [Trunov, A. B., & Polycarpou, M. M. (2000). Automated fault diagnosis in nonlinear multivariable systems using a learning methodology. IEEE Transactions on Neural Networks, 11, 91–101], a novel adaptive observer for detecting and estimating faults in the considered system is constructed, and robustness with respect to unknown inputs and sensitivity to faults of the detecting scheme are rigorously analyzed. The fault estimate and the state estimation error are then proved to be uniformly bounded. Finally, simulations of a heating process demonstrate that the proposed approach can detect the faults shortly after the occurrences without any false alarm and can approximate the faults with desired accuracy.
Fault detection and identification, Time-delay systems, Adaptive observers, Uncertain systems
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86浏览
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259下载
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【期刊论文】Fast and robust fault diagnosis for a class of nonlinear systems: detectability analysis
周东华, Linglai Li, Donghua Zhou*
Computers and Chemical Engineering 28(2004)2635-2646,-0001,():
-1年11月30日
In recent years, robust fault diagnosis of nonlinear systems has received much more attention due to the universal existence of nonlinearities and model uncertainties in practice. By introducing a new adaptive law and sliding mode observers with boundary layer control into Polycarpou's online approximator, we propose a fast and robust fault diagnosis strategy for a class of nonlinear systems in this article. The robustness and stability are proved theoretically by the Lyapunov method and the detectability conditions as well as the upper bound of detection time are given, which demonstrate that the detection time of our strategy is much shorter than that of Polycarpou's approach. Simulation results on the three-tank system "DTS200" show the effectiveness and fastness of the proposed strategy.
Fault diagnosis, Nonlinear systems, Robustness, Detectability, Online approximator
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60浏览
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247下载
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引用
周东华, Yan Lianga, De Xi Ana, Dong Hua Zhoua, *, Quan Panb
Computers and Chemical Engineering 27(2003)1761-1778,-0001,():
-1年11月30日
It is a challenging problem to estimate time-varying time delay and parameters, especially for systems subject to disturbances with unknown statistics in measurements. The desirable filter should be sensitive to unmodeled dynamics caused by random changes in time delay and parameters, and also be robust to disturbances. Recently, we proposed a finite-horizon robust Kalman filter (RKF) through designing and simultaneously minimizing the upper bounds of unknown covariances of prediction errors, filtering residuals and estimation errors. Unfortunately, unmodeled dynamics and disturbances must be hypothesized to be zero-mean white noises in the RKF. To cope with more general unmodeled dynamics and/or disturbances, a class of jump Markov stochastic systems (JMSS) subject to unmodeled dynamics and disturbances is considered in this article so that a priori system information, such as the value range of unknown and/or randomly changing parameters, can be introduced. Through combining the RKF with the interacting multiple model (IMM) estimation technique, a RKF/IMM algorithm is proposed for such JMSS. Then it is applied to estimate timevarying time delay and parameters of a continuous stirred tank reactor (CSTR) with sensors subject to Gaussian disturbances with unknown means and/or covariances. The RKF/IMM algorithm is compared with the extended Kalman filter (EKF), the strong tracking filter (STF) and the RKF through computer simulations. The results show that, in the case that measurement disturbances are zero-mean noise with unknown covariances, the RKF/IMM and RKF achieve almost the same accurate estimates, which are superior to those of the STF and EKF; in the case that such disturbances have unknown covariances and time-varying means, only the RKF/IMM remains the ability to estimate time-varying time delay and parameters. Furthermore the RKF/IMM has unique ability to identify the disturbance mean no matter whether it is constant or time-varying. Moreover, the RKF/IMM algorithm is shown having strong robustness against the a priori filter parameters, this may be attractive in practical applications.
Time delay estimation, Parameter estimation, Robust filters, Multiple model estimation, Stochastic systems
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172浏览
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189下载
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引用
周东华, D. Wanga, *, D.H. Zhoua, Y.H. Jina, S.J. Qinb
Journal of Process Control 14(2004)517-531,-0001,():
-1年11月30日
In this article, an adaptive control approach–Adaptive Generic Model Control (AGMC) for a class of nonlinear time-varying processes with input time delay is proposed. First, a nonlinear state predictor (NSP) is introduced, which extends the conventional generic model control (GMC) to a class of nonlinear processes with input time delay. Then a class of nonlinear time-varying processes with input time delay is further considered. A modified strong tracking filter (MSTF) is adopted to estimate the timevarying parameters of the nonlinear processes, and the state estimates are then utilized to update the plant models used in the NSP and MSTF, this results in an adaptive generic model control scheme for a class of nonlinear time-varying processes with input time delay. A modified mathematical model of a three-tank-system is used for computer simulations, the results show that the proposed AGMC algorithm is satisfactory, and it has definite robustness against model/plant mismatch in the measurement noise.
Adaptive control, Generic model control, Nonlinear processes, Time-varying processes, State predictor, Modified strong tracking filter
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50浏览
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【期刊论文】Adaptive generalized generic model control and stability analysis
周东华, D. Wanga, D.H. Zhoua, b, *, Y.H. Jina, A.S. Morseb
Computers and Chemical Engineering 27(2003)1617-1629,-0001,():
-1年11月30日
In this article, an adaptive control method-adaptive generalized generic model control (AGGMC) is proposed for a class of nonlinear time-varying processes by use of a modified strong tracking filter (MSTF). It inherits all of the advantages of generic model control (GMC) and extends GMC to nonlinear time-varying processes with relative orders larger than ones. Sufficient conditions under which the MSTF is asymptotically convergent are first derived, then, with these conditions, the resultant closedloop system under the AGGMC is proved to be Lyapunov stable. Finally, simulation studies are provided to validate the effectiveness of the proposed approach.
Generic model control, Nonlinear systems, Time-varying systems, Strong tracking filter, Adaptive control, Stability analysis, Lyapunov stability
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53浏览
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【期刊论文】A time-delayed method for controlling chaotic maps
周东华, Maoyin Chena, * Donghua Zhoua, Yun Shangb
Physics Letters A 348(2005)37-43,-0001,():
-1年11月30日
Combining the repetitive learning strategy and the optimality principle, this Letter proposes a time-delayed method to control chaotic maps. This method can effectively stabilize unstable periodic orbits within chaotic attractors in the sense of least mean square. Numerical simulations of some chaotic maps verify the effectiveness of this method.
Chaotic maps, Repetitive learning, Optimality, Time-delayed
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63浏览
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【期刊论文】A strong tracking predictor for nonlinear processes with input time delay
周东华, D. Wanga, D.H. Zhoua, *, Y.H. Jina, S. Joe Qinb
Computers and Chemical Engineering 28(2004)2523-2540,-0001,():
-1年11月30日
Nonlinear state prediction is of crucial importance to design controllers for nonlinear processes with input time delay. In this paper, the extended nonlinear state predictor (ENSP) we proposed is first outlined, which is used to predict the future states of a class of nonlinear processes with input time delay. A new concept of strong tracking predictor (STP) is then proposed, and an rthogonality principle is given as a criterion to design the STP. On the basis of the orthogonality principle, the ENSP is modified, which results in a STP. After the detailed STP algorithm is presented, we prove that the STP is locally asymptotically convergent for a class of nonlinear deterministic processes if some sufficient conditions are satisfied. In the presence of measurement noise, it is further proved that the proposed STP is exponentially bounded under certain conditions. Finally, computer simulations with a MIMO nonlinear model are presented, which illustrate that the proposed STP can predict accurately the future states of a class of nonlinear time delay processes no matter whether the states change suddenly or slowly, in addition, it has definite robustness against model/plant mismatches.
Nonlinear processes, nput time delay, State predictor, Extended Kalman filter, Orthogonality principle, Strong tracking predictor, Convergence analysis
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【期刊论文】A real-time estimation approach to time-varying time delay and parameters of NARX processes
周东华, D.H. Zhoua, *, P.M. Frankb
Computers and Chemical Engineering 23(2000)1763-1772,-0001,():
-1年11月30日
This paper presents a solution to the joint time-varying time delay and parameter estimation of NARX (nonlinear autoregressive with exogenous inputs) processes, where only pure time delay in input signal is considered. A modified strong tracking filter (MSTF) is proposed, and is adopted as an adaptive estimation algorithm. Three kinds of specific NARX processes are considered. The first is also the simplest, the output signal is the input with time delay plus disturbance; The second one is a simple NARX process plus disturbance; The third NARX process even has unknown time-varying parameters. For each of the NARX processes, we set up a specific estimation model, with these models the proposed MSTF algorithm can be applied to the real-time time delay and parameter estimation of the above three NARX processes. Computer simulation results demonstrate the effectiveness of the proposed approach. Moreover the robustness of the proposed algorithm against some model:process parameter mismatches is also tested via computer simulations.
Nonlinear processes, ime-varying, ime-delay, Parameters, stimation, trong tracking filter
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