State estimation for descriptor systems via the unknown input filtering method

Hsieh, Chien-Shu

doi:10.1016/j.automatica.2013.01.049

Cited by 63 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main idea to solve the CSISE problem is to apply the descriptor system reformation [9] to the original system in order to derive an equivalent augmented state system [12] which is free of unknown inputs.…”

Section: Equivalent Augmented State Systemmentioning

confidence: 99%

“…Furthermore, a common approach to facilitate the above UIOs design is to use state transformation techniques or complex matrix equations solving. On the other hand, in the present paper we adopt a straightforward but more compact method, which is originated from the discrete-time unknown input filtering (UIF) [7]- [9], to propose a system augmentation approach to solve the SISE problem. Unlike those mentioned above, where the estimators for the state and unknown inputs are derived independently, the proposed new method determines the augmented state estimator which jointly estimates the state and unknown inputs estimates.…”

Section: Introductionmentioning

confidence: 99%

“…Simultaneous input and state estimation (SISE) has received much research attention due to its vast applications in different research areas, e.g., input reconstruction [1], [2], input estimation in physiological systems [3], fault diagnosis [4], [5], robust state estimation [6]- [8], and descriptor state estimation [9]. Most of the above work are focused on either discrete-time systems or time-invariant continuous-time systems, and very few addressed for time-varying continuoustime systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

Hsieh¹

2016

Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

View full text Add to dashboard Cite

Abstract-This paper presents an augmented state observer design for linear time-varying continuous-time systems with unknown inputs. An equivalent augmented state system, which is free of unknown inputs, is derived for the purpose of simultaneous input and state estimation (SISE). It is shown that, through the proposed system augmentation approach, the observer design for SISE problems can be easily solved using well-known techniques. The existence condition of SISE designs is also proposed. An illustrative example is given to show the effectiveness of the proposed results.

show abstract

Section: Equivalent Augmented State Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

Hsieh¹

2016

Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

View full text Add to dashboard Cite

show abstract

“…In addition, the external interferences and syntheses of device failures have effect on the sensor outputs, which results in the uncertainties of measurement outputs. In recent years, the state estimation for systems with unknown inputs has also become a hot research topic [9][10][11], especially in the application of fault diagnosis. A fault diagnosis method based on the optimal unknown input observer is presented in [12].…”

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts

Bo¹,

Sun²

2016

Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer

View full text Add to dashboard Cite

Abstract. This paper is concerned with the design of distributed fusion filter for networked systems with unknown measurement interferences and packet dropouts. A Bernoulli distributed random variable is used to depict the phenomenon of packet dropouts. Without any prior information about the interference, a recursive Kalman-type state filter independent of the unknown interferences is designed for each sensor subsystem by applying the linear unbiased minimum variance estimation criterion. Based on the state filters of individual subsystems, the estimation error cross-covariance matrices between any two subsystems are derived. Then, the distributed fusion filter is designed by using the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.

show abstract

“…Some research works on state estimation for discrete-time descriptor systems have been carried out (see as e.g. [10], [11], where system states are estimated by using different versions of Kalman filtering).…”

Section: Introductionmentioning

confidence: 99%

Set-membership approach and Kalman observer based on zonotopes for discrete-time descriptor systems

2018

View full text Add to dashboard Cite

This paper proposes a set-membership state estimator and a zonotopic Kalman observer for discrete-time descriptor systems. Both approaches are developed in a set-based context considering system disturbances, measurement noise, and unknown inputs. This set-membership state estimation approach determines the set of consistent states with the model and measurements by constructing a parameterized intersection zonotope. Two methods to minimize the size of this intersection zonotope are provided: one inspired by Kalman filtering and the other based on solving an optimization problem involving a series of linear matrix inequalities. Additionally, we propose a zonotopic Kalman observer for discrete-time descriptor systems. Moreover, the relationship between both approaches is discussed. In particular, it is proved that the zonotopic Kalman observer in the current estimation type is equivalent to the set-membership approach. Finally, a numerical example is used to illustrate and compare the effectiveness of the proposed approaches.

show abstract

State estimation for descriptor systems via the unknown input filtering method

Cited by 63 publications

References 21 publications

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts

Set-membership approach and Kalman observer based on zonotopes for discrete-time descriptor systems

Contact Info

Product

Resources

About