Broadband Load Torque Estimation in Mechatronic Powertrains Using Nonlinear Kalman Filtering

Forrier, Bart; Naets, Frank; Desmet, Wim

doi:10.1109/tie.2017.2739709

Cited by 36 publications

(32 citation statements)

References 24 publications

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“…In addition, a fuzzy modeling approach can be combined with the EKF [26]. Other applications of EKF include mechatronic powertrains [27], a wave energy converter (WEC) [28], and positioning systems [29]. Even though both stochastic nonlinearities and multiple missing measurements are often induced in actual systems simultaneously, the novel extended Kalman filter proposed by [30] can compensate for the effects due to them, resulting in well state estimation performance.…”

Section: It Is Necessary To Combine Traditional Approaches Withmentioning

confidence: 99%

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Application of Physical Function Model to State Estimations of Nonlinear Mechanical Systems

et al. 2021

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The physical function model has been effectively used for model-based development (MBD) of automobile systems. This research demonstrates a novel application of this modeling method to the state estimation of nonlinear mechanical systems based on the Kalman filtering theory. The physical function model is a block diagram that describes each engineering field by a common rule, which focuses on the energy flow. Compared to traditional modeling approaches, this model has the flexibility to incorporate a wide range of nonlinear characteristics and the know-how accumulated by the manufacturers. Hence, it has a quite high affinity with the industrial world. The purpose of this research is to pioneer a new application of the physical function model beyond simulation analysis. In particular, physical function modeling offers a model of a system with multiple nonlinearities in the form of a time-varying linear state equation. By focusing on this feature, this study applies it to the Kalman filtering theory. The proposed approach is applicable to a wide range of nonlinearities, reduces the calculation load, and considers the background of the current MBD. Finally, verifications using an experimental apparatus, which simplifies an automotive drivetrain with backlash, demonstrate the effectiveness of the proposed approach.

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Section: It Is Necessary To Combine Traditional Approaches Withmentioning

confidence: 99%

“…corresponding to (27). In the experiments below, the covariance matrices were adjusted as Q = q × I 4×4 = 10 8 × I 4×4 ∼ 10 9…”

Section: B Time-varying Linear Kalman Filter Based On the Physical Function Modelmentioning

confidence: 99%

Application of Physical Function Model to State Estimations of Nonlinear Mechanical Systems

et al. 2021

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“…In mechatronic systems, operational forces and moments are essential quantities in the different stages of the development cycle and strongly impact the design, durability, diagnostic, prognostic, maintenance, and advanced control strategies [ 1 ]. However, forces and moments are also difficult, even impossible, quantities to measure.…”

Section: Introductionmentioning

confidence: 99%

A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

Adduci

Vermaut

Naets

et al. 2021

Sensors

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Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.

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“…Nonlinear filtering techniques have been researched for overcoming the disadvantages of linear methods. For example, Kalman filters [1]- [3] are widely applied for removing noise. However, they may diverge in the cases of large initial errors, and their performance depends on the accuracy of the system model.…”

Section: Introductionmentioning

confidence: 99%

A Chattering-Free Sliding Mode Filter Enhanced by First Order Derivative Feedforward

Jin

Xiong

et al. 2020

IEEE Access

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Noise reduction is one of the important issues for feedback control systems. Toward this problem, this paper proposes a new sliding mode filter, which is an improvement of Lv et al.'s parabolic sliding mode filter. The proposed filter employs a first-order derivative feed-forward term for increasing both tracking and noise attenuating performances. Its discrete-time algorithm is derived by applying the implicit-Euler discretization, and it realizes exact convergence without producing chattering in discrete-time implementation. The effectiveness of the proposed filter is evaluated through an open-loop and a closed-loop numerical examples. INDEX TERMS Sliding mode filter, implicit Euler discretization, chattering avoidance, noise reduction, PD control, feedback control.

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Broadband Load Torque Estimation in Mechatronic Powertrains Using Nonlinear Kalman Filtering

Cited by 36 publications

References 24 publications

Application of Physical Function Model to State Estimations of Nonlinear Mechanical Systems

Application of Physical Function Model to State Estimations of Nonlinear Mechanical Systems

A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

A Chattering-Free Sliding Mode Filter Enhanced by First Order Derivative Feedforward

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