On sliding mode observers for systems with unknown inputs

Floquet, Thierry; Edwards, C.; Spurgeon, Sarah K.

doi:10.1002/acs.958

Cited by 232 publications

(109 citation statements)

References 33 publications

(30 reference statements)

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“…However, existing PEA models, such as the one in [92], do not meet this condition. Attempts to relax the observer matching condition have been reported [156,157], but the resultant UIOs were very complicated. As such, a UIO with a simpler structure and the capability of relaxing the observer matching condition still needs to be developed in future research for the use in PEA tracking control.…”

Section: State Estimationmentioning

confidence: 99%

“…Applications of these UIOs require that the observer matching condition be satisfied [156,157], which states that the rank of the product of the output matrix and the unknown input matrix in the state space model of the system must be equal to that of the unknown input matrix [110]. However, existing PEA models, such as the one in [92], do not meet this condition.…”

Section: State Estimationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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Piezoelectric actuators (PEAs) have been widely used in micro-and nanopositioning applications due to their fine resolution, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis makes modeling and control of PEAs challenging. This paper reviews the recent achievements in modeling and control of piezoelectric actuators. Specifically, various methods for modeling linear and nonlinear behaviors of PEAs, including vibration dynamics, hysteresis, and creep, are examined; and the issues involved are identified. In the control of PEAs as applied to positioning, a review of various control schemes of both model-based and non-model-based is presented along with their limitations. The challenges associated with the control problem are also discussed. This paper is concluded with the emerging issues identified in modeling and control of PEAs for future research.

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Section: State Estimationmentioning

confidence: 99%

Section: State Estimationmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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“…Comparing to the original form in Equation (1), it gives n = n + p + 1, m = m, q = q, r = r, and p = p + 1. For Equation (7), with health parameters treated as unknown inputs, the idea is to apply sliding mode observer to estimating performance degradation via "fault reconstruction" technique, like described in [27][28][29]. As argued in [30], the necessary and sufficient conditions for the existence of a stable sliding motion and feasibility of fault reconstruction are:…”

Section: X(t)mentioning

confidence: 99%

“…However, the approximation of the equivalent injections by low pass filter at each step will typically introduce some delays that lead to inaccurate estimations or to instability for high order systems [29]. In this special application, a simpler way is to adjust the outputs to render first Markov parameter full rank.…”

Section: X(t)mentioning

confidence: 99%

Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

2017

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Abstract:In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a "reconstruction signal" to estimate health parameters modeled as artificial inputs, and is not only applicable to long-time health degradation, but reacts much quicker in handling abrupt fault cases. In view of the inevitable uncertainties in engine dynamics and modeling, a weighting matrix is created to minimize such effect on estimation by using the linear matrix inequalities (LMI). A big step toward uncertainty modeling is taken compared with our previous SMO-based work, in that uncertainties are considered in a more practical form. Moreover, to avoid chattering in sliding modes, the super-twisting algorithm (STA) is employed in observer design. Various simulations are carried out, based on the comparisons between the KF-based scheme, the SMO-based scheme in our earlier research, and the proposed method. The results consistently demonstrate the capabilities and advantages of the proposed approach in health parameter estimation.

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“…To generate a residual signal, we design the following sliding mode observer [6,18] for the system model (1) aŝ…”

Section: Problem Formulationmentioning

confidence: 99%

Sliding Mode Observer-Based Fault Detection of Distributed Networked Control Systems with Time Delay

Zong

Zeng

Liu

et al. 2011

Circuits Syst Signal Process

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This paper considers the fault detection problem of distributed networked control systems (DNCS) with time delay. A sliding mode observer-based fault detection method for a two-level DNCS is presented and two different situations are considered. When all the states of the system are available for measurement, we convert the fault detection problem to a sliding motion stable and reachable problem. When some states of system are not available for measurement, we design a transformation matrix to separate the measurable states and the unknown states, and then different sliding mode observers for those unknown states are developed to achieve fault detection. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method with simulation results.

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On sliding mode observers for systems with unknown inputs

Cited by 232 publications

References 33 publications

A Survey of Modeling and Control of Piezoelectric Actuators

A Survey of Modeling and Control of Piezoelectric Actuators

Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

Sliding Mode Observer-Based Fault Detection of Distributed Networked Control Systems with Time Delay

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