Parameter Identification for a High-Performance Hydrostatic Actuation System Using the Variable Structure Filter Concept

2006 ◽  
Vol 129 (2) ◽  
pp. 229-235 ◽  
Author(s):  
S. R. Habibi ◽  
R. Burton
Keyword(s):  
Parameter Estimation ◽  
Early Detection ◽  
State Estimation ◽  
Condition Monitoring ◽  
High Performance ◽  
Variable Structure ◽  
Maintenance Costs ◽  
Estimation Strategy ◽  
Actuation System ◽  
Smooth Variable Structure Filter

Parameter estimation is an important concept that can be used for health and condition monitoring. Estimation or measurement of physically meaningful parameters and their evaluation against predetermined thresholds allows detection of gradual or abrupt deteriorations in the plant. This early detection of faults enables preventative unscheduled maintenance that is of benefit to industries concerned with reliability and safety. In this paper, a recently proposed state estimation strategy referred to as the smooth variable structure filter (SVSF) is reviewed and extended to parameter estimation. The SVSF is applied to a novel hydrostatic actuation system referred to as the electrohydraulic actuator (EHA). Condition monitoring of the EHA for preventative unscheduled maintenance would increase its safety in applications pertaining to aerospace and would reduce its operational and maintenance costs.

Parameter Identification for a High Performance Hydrostatic Actuation System Using the Variable Structure Filter Concept

2004 ◽  
Author(s):  
S. R. Habibi ◽  
R. Burton
Keyword(s):  
Parameter Estimation ◽  
Early Detection ◽  
State Estimation ◽  
Condition Monitoring ◽  
High Performance ◽  
Variable Structure ◽  
Maintenance Costs ◽  
Estimation Strategy ◽  
Actuation System ◽  
Smooth Variable Structure Filter

Parameter estimation is an important concept that can be used for health and condition monitoring. Estimation or measurement of physically meaningful parameters and their evaluation against predetermined thresholds allows detection of gradual or abrupt deteriorations in the plant. This early detection of faults enables preventative unscheduled maintenance that is of benefit to industries concerned with reliability and safety. In this paper, a recently proposed state estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) is reviewed and extended to parameter estimation. The SVSF is applied to a novel hydrostatic actuation system referred to as the ElectroHydraulic Actuator (EHA). Condition monitoring of the EHA for preventative unscheduled maintenance would increase its safety in applications pertaining to aerospace and would reduce its operational and maintenance costs.

Condition Monitoring of an Electro-Hydrostatic Actuator Using the Dynamic 2nd-Order Smooth Variable Structure Filter

2015 ◽  
Author(s):  
Hamed Hossein Afshari ◽  
Stephan Andrew Gadsden ◽  
Saeid Habibi
Keyword(s):  
Measurement Error ◽  
State Estimation ◽  
Condition Monitoring ◽  
Sliding Mode ◽  
Variable Structure ◽  
Second Order ◽  
Estimation Methods ◽  
Scenarios Simulation ◽  
Existence Subspace ◽  
Smooth Variable Structure Filter

This paper introduces the dynamic 2nd-order Smooth Variable Structure Filter (Dynamic 2nd-order SVSF) method for the purpose of robust state estimation. Thereafter, it presents an application of this method for condition monitoring of an electro-hydrostatic actuator system. The SVSF-type filtering is in general designed based on the sliding mode theory; whereas the sliding mode variable is equal to the innovation (measurement error). In order to formulate the dynamic 2nd-order SVSF, a dynamic sliding mode manifold is defined such that it preserves the first and second order sliding conditions. This causes that the measurement error and its first difference are pushed toward zero until reaching the existence subspace. Hence, this filter benefits from the robustness and chattering suppression properties of the second order sliding mode systems. These help the filter to suppress the undesirable chattering effects without the need for approximation or interpolation that however reduces accuracy and robustness of the SVSF-type filtering. In order to investigate the performance of the dynamic 2nd-order SVSF for state estimation, it applies to an Electro-Hydrostatic Actuator (EHA) system under the normal and uncertain scenarios. Simulation results are then compared with ones obtained by other estimation methods such as the Kalman filter and the 1st-order SVSF method.

scholarly journals Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8560
Author(s):  
Sara Rahimifard ◽  
Saeid Habibi ◽  
Gillian Goward ◽  
Jimi Tjong
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Variable Structure ◽  
Internal Resistance ◽  
Accurate Estimation ◽  
Battery Management ◽  
Robust Adaptive ◽  
State Of Power ◽  
Smooth Variable Structure Filter

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

Sliding Mode Controller and Filter Applied to a Model oft an Electrohydraulic Actuator System

2005 ◽  
Author(s):  
Shu Wang ◽  
Richard Burton ◽  
Saeid Habibi
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode Control ◽  
Stochastic Systems ◽  
Sliding Mode ◽  
Variable Structure ◽  
Variable Structure Systems ◽  
Estimation Strategy ◽  
Mode Control ◽  
Actuator System ◽  
Electrohydraulic Actuator

A new robust state and parameter estimation strategy called the Variable Structure Filter (VSF) has recently been proposed and used for state and parameter estimation. A very common problem of linear stochastic systems is to design a combined robust control and estimation strategy, given system and noise uncertainties. Variable Structure Control (VSC) and its special form of Sliding Mode Control (SMC) show superb robustness. This paper proposes a new strategy involving the Sliding Mode Control and the Variable Structure Filter. Both the estimator and controller are based on the concepts of Variable Structure Systems (VSS). In the presence of bounded parametric uncertainties and noise, a robust stability is guaranteed. Further more, the combined strategy can be used to achieve high regulation rates or short settling times. The object of this paper is to introduce this combined VSF and SMC strategy and to demonstrate its application to a third order model of a high precision hydrostatic system, referred to as the Electrohydraulic Actuator System (EHA).

Friction Fault Detection of an Electrohydrostatic Actuator

2011 ◽  
Author(s):  
S. A. Gadsden ◽  
Y. Song ◽  
K. R. McCullough ◽  
S. R. Habibi
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Sliding Mode ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Estimation Strategy ◽  
Operating Modes ◽  
Smooth Variable Structure Filter ◽  
Novel Model

This article discusses the application of a novel model-based fault detection method. The method is based on the interacting multiple model (IMM) strategy, which makes use of a finite number of known operating modes. A filter is used in conjunction with the IMM in order to estimate the states and parameters of the system. The smooth variable structure filter (SVSF) is a relatively new estimation strategy, and is based on sliding mode concepts which introduces an inherent amount of robustness and stability. The combined SVSF-IMM strategy is applied on an electrohydrostatic actuator (EHA), which is a device used in the aerospace industry. Two different operating modes were created, based on varying degrees of friction acting on the EHA cylinder. The results of the friction fault detection were compared with the popular Kalman filter (KF) based IMM strategy.

scholarly journals On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

2020 ◽  
Vol 6 ◽  
Author(s):  
Mark Spiller ◽  
Dirk Söffker
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Parameter Optimization ◽  
Estimation Error ◽  
Variable Structure ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Kalman Filter ◽  
Model Uncertainties ◽  
Smooth Variable Structure Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.

A Multi-Target Tracking Formulation of SVSF With the Joint Probabilistic Data Association Technique

2014 ◽  
Author(s):  
Mina Attari ◽  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Parameter Estimation ◽  
Target Tracking ◽  
Data Association ◽  
Joint Probability ◽  
Variable Structure ◽  
Tracking Problem ◽  
Proof Of Concept ◽  
Multi Target Tracking ◽  
Smooth Variable Structure Filter ◽  
Parameter Estimation Techniques

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).

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