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Gan M.,Hefei University of Technology | Li H.-X.,City University of Hong Kong | Li H.-X.,Central South University | Peng H.,Central South University | Peng H.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha
IEEE Transactions on Cybernetics | Year: 2015

The radial basis function network-based autoregressive with exogenous inputs (RBF-ARX) models have much more linear parameters than nonlinear parameters. Taking advantage of this special structure, a variable projection algorithm is proposed to estimate the model parameters more efficiently by eliminating the linear parameters through the orthogonal projection. The proposed method not only substantially reduces the dimension of parameter space of RBF-ARX model but also results in a better-conditioned problem. In this paper, both the full Jacobian matrix of Golub and Pereyra and the Kaufman's simplification are used to test the performance of the algorithm. An example of chaotic time series modeling is presented for the numerical comparison. It clearly demonstrates that the proposed approach is computationally more efficient than the previous structured nonlinear parameter optimization method and the conventional Levenberg-Marquardt algorithm without the parameters separated. Finally, the proposed method is also applied to a simulated nonlinear single-input single-output process, a time-varying nonlinear process and a real multiinput multioutput nonlinear industrial process to illustrate its usefulness. © 2014 IEEE. Source


Xiao J.,Central South University | Xiao J.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | Liu W.,Central South University | Liu W.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | And 3 more authors.
26th Chinese Control and Decision Conference, CCDC 2014 | Year: 2014

Although prolonging network life time in target tracking has been extensively studied in the literature of wireless sensor networks, the problem of energy efficiency with well tracking performance is still an open issue. In lots of exited works about target tracking in WSN, energy cost and uniform sampling interval was taken into consideration. While it ignored the remaining energy selected sensors as well as the changing of the target dynamic. In this paper, a target tracking scheme based on minimizing the ratio between the total energy cost and the remaining energy and maximizing the sampling interval is proposed. It contains two parts: the target sensing based on the Kalman-Consensus filer that active sensors receive/broadcast information with its active neighbors and estimate the position of target, the sensors scheduling scheme that selecting the tasking sensors by a predicted objective function. In this function, the tradeoff between tracking performance and network energy costs is obtained. Simulation results show its better performance in target tracking. © 2014 IEEE. Source


Yu Z.,Central South University | Yu Z.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | Jun P.,Central South University | Jun P.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | And 6 more authors.
26th Chinese Control and Decision Conference, CCDC 2014 | Year: 2014

In this paper, we consider the problems of fault detection for heavy-haul trains equipped with electronically controlled pneumatic (ECP) brake systems. A longitudinal dynamical model which has been successfully validated is used to simulate the actual situation. Based on the model, a set of unknown input observers which are adopted to estimate locomotives' state is constructed, and observers can determine the existence and place of the faulty locomotive. Since heavy-haul trains are much longer than general passenger trains, the longitudinal dynamical model is decomposed into smaller subsystems which can be detected locally. To estimate the fault parameter after a failure occurred, a minimal extremum seeking algorithm was presented for adaptive approximation. Simulation results provide evidence of the effectiveness of the proposed fault detection scheme. © 2014 IEEE. Source


Qin Y.,Central South University | Qin Y.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | Peng J.,Central South University | Peng J.,Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha | And 6 more authors.
26th Chinese Control and Decision Conference, CCDC 2014 | Year: 2014

In this paper, the fault detection and isolation problems (FDI) of heavy-haul trains equipped with electronically controlled pneumatic (ECP) brake system are studied. A longitudinal dynamical model and the fault modes of trains are considered. A fault detection estimator bases on the nonlinear observer is designed to generate residual for detecting fault. After the fault is detected, a group of fault isolation estimators with different residuals and thresholds can be designed, each estimator corresponds to one possible actuator fault of trains. And in each fault isolation estimator, the unknown actuator fault of trains can be estimate simultaneously by introduce a learning algorithm. A simulation analysis is proposed to show the validity of the designed FDI scheme, by using the parameters from the heavy-haul trains system running on Datong-Qinhuangdao railway in China. © 2014 IEEE. Source

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