Time filter

Source Type

Liu A.,Zhejiang University of Technology | Liu A.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Liu A.,City University of Hong Kong | Zhang W.-A.,Zhejiang University of Technology | And 4 more authors.
IEEE Transactions on Industrial Electronics | Year: 2017

This paper investigates the multirate moving horizon estimation (MMHE) problem for mobile robots with inertial sensor and camera, where the sampling rates of the sensors are not identical. In the sense of the multirate systems, some sensors may have no measurements at certain sampling times, which can be regarded as measurement missing and may significantly degrade the estimation performance. A binary switching sequence is introduced to model the multirate sampling process, and an explicit mathematical description of this process is proposed where a prediction value activated by a prediction generator is used for the output of missing sampling for slow measurement. The prediction generator provides a set of predictions to make the estimation system achieve the desired performance. By choosing a cost function, the optimal estimator is obtained by solving a regularized least-squares problem with unconstraints. The constrained MMHE problem is studied by using the interior-point algorithm. The input-to-state stability is investigated for the optimal estimator in the presence of bounded disturbances and noises. Finally, a mobile robot tracking platform is designed, and both simulations and experiments are presented to demonstrate the effectiveness of the proposed method. © 2016 IEEE.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2011

The robust Kalman filtering problem is investigated in this paper for linear uncertain stochastic systems with state delay, observation delay, and missing measurement. For robust performance, stochastic parameter perturbations are considered in the system matrix. The missing measurement can be described by a Bernoulli distributed random variable and its probability is assumed to be known. Based on the minimum mean square error (MMSE) estimation principle, a new filter design method is proposed by using the projection theory. The dimension of the designed filter is the same as the original systems. Compared with conventional state augmentation, the presented approach greatly lessens the computational demand when the delay is large. A simulation example is given to illustrate the effectiveness of the proposed approach. © 2011 Acta Automatica sinica. All rights reserved.


Song H.,Zhejiang University of Technology | Song H.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
IET Control Theory and Applications | Year: 2014

This study investigates the multi-sensor-based centralised estimation problem in heterogeneous sensor networks with a common communication channel. Owing to the heterogeneity of the distributed sensors, it is usually impossible to package the measurements into one packet and transmit them to the fusion centre (FC) together, which implies that the measurements should be forwarded to the FC asynchronously. In view of this, a novel stochastic competitive transmission strategy is proposed to stagger the sensors' transmissions. By using the asynchronous sampled information from the sensors, an H ∞ filter is designed for the FC to periodically generate estimates. The filter parameters are determined by solving a linear-matrix inequality. An illustrative example is provided to demonstrate the effectiveness of the proposed theoretical results. © The Institution of Engineering and Technology 2014.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
Neural Computing and Applications | Year: 2011

This paper is concerned with the exponential stability analysis problem for a class of neutral bidirectional associative memory neural networks with mixed time-delays, where discrete, distributed and neutral delays are involved. By utilizing the delay decomposition approach and an appropriately constructed Lyapunov-Krasovskii functional, some novel delay-dependent and decay rate-dependent criteria for the exponential stability of the considered neural networks are derived and presented in terms of linear matrix inequalities. Furthermore, the maximum allowable decay rate can be estimated based on the obtained results. Three numerical examples are given to demonstrate the effectiveness of the proposed method. © 2010 Springer-Verlag London Limited.


Zhang W.-A.,Zhejiang University of Technology | Zhang W.-A.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems
International Journal of Robust and Nonlinear Control | Year: 2011

This paper presents a robust control approach to solve the stability and stabilization problems for networked control systems (NCSs) with short time-varying delays. A new discrete-time linear uncertain system model is proposed to describe the NCS, and the uncertainty of the network-induced delay is transformed into the uncertainty of the system matrix. Based on the obtained uncertain system model, a sufficient BIBO stability condition for the closed-loop NCS is derived by applying the small gain theorem. The obtained stability condition establishes a quantitative relation between the BIBO stability of the closed-loop NCS and two delay parameters, namely, the delay upper bound and the delay variation range bound. Moreover, design procedures for the state feedback stabilizing controllers are also presented. An illustrative example is provided to demonstrate the effectiveness of the proposed method. © 2010 John Wiley & Sons, Ltd.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 4 more authors.
2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012 | Year: 2012

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example. © 2012 IEEE.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
Journal of Control Theory and Applications | Year: 2012

This paper is concerned with the global exponential stability analysis problem for a class of neutral bidirectional associative memory (BAM) neural networks with time-varying delays and stochastic disturbances. The stochastic disturbances are described by state-dependent stochastic processes. By utilizing an appropriately constructed Lyapunov-Krasovskii functional (LKF) and some stochastic analysis approaches, novel delay-dependent conditions are established in terms of linear matrix inequalities (LMIs), which can be easily solved by existing convex optimization techniques. Furthermore, the exponential convergence rate can be estimated based on the obtained results. An illustrate example is given to demonstrate the effectiveness of the proposed methods. © 2012 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Zhang W.-A.,Zhejiang University of Technology | Zhang W.-A.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
IEEE Transactions on Automatic Control | Year: 2014

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with missing sensor measurements, random transmission delays and packet dropouts. A novel stochastic model is proposed to describe the transmission delays and packet dropouts, and an optimal distributed fusion Kalman filter (DFKF) is designed based on the optimal fusion criterion weighted by matrices. Some sufficient conditions are derived such that the MSE of the designed DFKF is bounded or convergent. Moreover, steady-state DFKF is also presented for the NMFSs. An illustrative example is given to demonstrate the effectiveness of the proposed results. © 1963-2012 IEEE.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
Circuits, Systems, and Signal Processing | Year: 2011

This paper is concerned with the H ∞ filtering problem for a class of nonlinear Markovian switching genetic regulatory networks (GRNs) with time-delays, intrinsic fluctuation and extrinsic noise. The delays, which exist in both the translation process and feedback regulation process, are not dependent on the system model. The intrinsic fluctuation is described as a state-dependent stochastic process, while the extrinsic noise is modeled as an arbitrary signal with bounded energy, and no exact statistics about the noise are required to be known. The aim of the problem addressed is to design a Markovian jump linear filter to estimate the true concentrations of mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, then the desired linear filter exists. The designed filter ensures asymptotic mean-square stability of the filtering error system and two prescribed L 2-induced gains from the noise signals to the estimation errors. Finally, an illustrative example is given to demonstrate the effectiveness of the approach proposed. © 2011 Springer Science+Business Media, LLC.


Chen B.,Zhejiang University of Technology | Chen B.,Zhejiang Provincial United Key Laboratory of Embedded Systems | Yu L.,Zhejiang University of Technology | Yu L.,Zhejiang Provincial United Key Laboratory of Embedded Systems | And 2 more authors.
IET Control Theory and Applications | Year: 2011

The robust Kalman filtering problem is investigated for uncertain stochastic systems with time-invariant state delay d0, bounded random observation delays and missing measurements. The described model is generalised to the case that d0≠d1, where d1 denotes the upper bound of random observation delays. The random delays and missing measurements are described by multiple Bernoulli random processes and their probabilities are assumed to be known. For robust performance, stochastic parameter perturbations are considered. Unlike the system augmentation approach, the robust Kalman filtering is derived in the linear minimum variance sense by using the innovation analysis approach, and the dimension of the designed filter is the same as the original systems. Moreover, the performance of the designed filter is dependent on the probabilities of delays and missing measurements at each step. An illustrative example is presented to demonstrate the effectiveness of the proposed design method. © 2011 The Institution of Engineering and Technology.

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