Optimal Filtering for Systems with Unknown Inputs Via Unbiased Minimum-Variance Estimation

Hsieh, Chien-Shu

doi:10.1109/tencon.2006.344113

Cited by 13 publications

(29 citation statements)

References 9 publications

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“…4, it can be seen that Kalman gives better estimation compared with UMVE. This is understandable because the derivation of UMVE, see Hsieh [20] only gives suboptimal estimation, it's estimation error is almost ten times compared with Kalman filter in state 2, and nearly the same in state 7. Output estimation result of UMVE is shown in Fig.…”

Section: Simulation Resultsmentioning

confidence: 91%

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Unknown-input estimator-based controller design of electric power-assisted steering system

Mahmoud

Emzir

2012

IET Control Theory Appl.

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In this study, the control design problem of double-pinion-type electric power assist steering (EPAS) is carefully examined. Based on a Lagrangian-based dynamical model, an optimal control approach with unknown input is formulated thereby eliminating the need for torque sensor. A new controller is developed using combination of non-linear assist curve, unknown-input estimator (UIE) and linear quadratic integral theory. It has been established that the estimation of the state, and unknown input using UIE yields a good estimate with performance compared with the well-known Kalman estimator. It is further shown that the resulting closed-loop response is able to track non-linear assist curve for different velocity. This reveals a salient feature, that is, controlling an EPAS system can be done using only single constant gain.

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Section: Simulation Resultsmentioning

confidence: 91%

“…This broad class of estimators or observers known as 'unknown-input' estimator. Several efforts have been spent to develop UIE [19] and here we will adopt the approach presented in [20]. To compute the state and disturbance estimations, the EPAS dynamic is first transferred to discrete time.…”

Section: Unknown Input Estimatormentioning

confidence: 99%

Unknown-input estimator-based controller design of electric power-assisted steering system

Mahmoud

Emzir

2012

IET Control Theory Appl.

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“…It is known that the optimal linear unbiased filter for system (37)-(38) under the assumption that the obtained filter z k is not affected by the unknown inputs d k is given by the optimal unbiased minimum-variance filter (OUMVF) [23]. However, the unbiasedness constraints in the OUMVF may be too restricted to be applied, and hence they may result in filtering performance degradation problems.…”

Section: Problem Formulationmentioning

confidence: 99%

“…It is known that the optimal filtering for systems with unknown inputs that have arbitrary statistics and affect both the system model and the measurements has played a significant role in many applications, e.g., geophysical and environmental applications [19] and fault detection and isolation problems [20]. There are many approaches in the literature to solve this kind of problem, e.g., the unbiased minimum-variance estimation [19]- [23], the equivalent system description method [24], the state estimation technique for descriptor systems [25], and the parameterized minimum-variance filtering (PMVF) [26]. In this paper, we shall show how to apply the PMVF to solve the considered NCS that has incurred parametric uncertainty and control failure problems.…”

Section: Introductionmentioning

confidence: 99%

Robust LQ reliable control for networked control systems with long time delays and missing measurements

Hsieh

2008

2008 IEEE International Conference on Industrial Technology

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This paper considers the design of robust LQ reliable control (RLQRC) for networked control systems (NCSs) whose network-induced delay is longer than a sampling period and the measured output may be lost. Both of the full state feedback and filter-based output feedback controls are considered. The derived controls may have the potentiality to tolerate control failures and sustain parametric uncertainties. A parameterized minimumvariance filter (PMVF) is presented for estimating the system state and control failures when the parametric uncertainties are modeled as unknown inputs. It is shown that the proposed RLQRC may be seen as an extended version of the existing stochastic optimal control.

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“…This paper considers the optimal filtering for systems with unknown inputs that affect both the system model and the measurements via unbiased minimum-variance estimation. In an early paper [8], we proposed the optimal unbiased minimumvariance filter (OUMVF) through which the direct relationship between the first two approaches is clearly illustrated. Specifically, the relationships with the existing literature results, i.e., [2], [6], are addressed.…”

Section: Introductionmentioning

confidence: 99%

Extension of the robust two-stage Kalman filtering for systems with unknown inputs

Hsieh

2007

TENCON 2007 - 2007 IEEE Region 10 Conference

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This paper considers the optimal unbiased minimumvariance estimation for systems with unknown inputs that affect both the system model and the measurements. By making use of the recently developed optimal unbiased minimum-variance filter (OUMVF) and a new proposed constrained relationship, an extension of the previous proposed robust two-stage Kalman filter (RTSKF), which is named as the ERTSKF, is presented. The proposed ERTSKF is the optimal solution of two-stage Kalman filters for the considered problem in the sense that it is equivalent to the OUMVF. This is formally established in a theorem. It is also shown that the ERTSKF is computationally more attractive than the OUMVF. Moreover, an alternative to the ERTSKF is also presented to further reduce the computational complexity. Simulation results confirm the effectiveness of the proposed results.

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Optimal Filtering for Systems with Unknown Inputs Via Unbiased Minimum-Variance Estimation

Cited by 13 publications

References 9 publications

Unknown-input estimator-based controller design of electric power-assisted steering system

Unknown-input estimator-based controller design of electric power-assisted steering system

Robust LQ reliable control for networked control systems with long time delays and missing measurements

Extension of the robust two-stage Kalman filtering for systems with unknown inputs

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