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The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are among the most popular estimation methods. The smooth variable structure filter (SVSF) is a relatively new sliding mode estimator. In an effort to use the accuracy of the EKF and the UKF and the robustness of the SVSF, the filters have been combined, resulting in two new estimation strategies, called the EK-SVSF and the UK-SVSF, respectively. The algorithms were validated by testing them on a well-known target tracking computer experiment. [ABSTRACT FROM AUTHOR]

This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observa-tions. These two target tracking problems provide a good bench-mark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its sta-bility and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters per-formances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accu-racy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlin-ear target tracking problems, which in the past have typically been solved by Kalman or particle filters. [ABSTRACT FROM AUTHOR]

A method that is often used for parameter estimation is the extended Kalman filter (EKF). EKF is a model-based strategy that implicitly considers the effect of modeling uncertainties. This implicit consideration often leads to the tuning of the filter by trial and error. When formulated for parameter estimation, the "tuned" EKF becomes sensitive to uncertainties in its internal model. The EKF's robustness can be improved by combining it with the recently proposed variable structure filter (VSF) concept. In a combined form, the modeling uncertainties no longer affect stability, but impact the performance and the quality of the estimation process. Furthermore, the VSF concept provides a secondary set of indicators of performance that is in addition to the estimation error and that pertains to the range of parametric uncertainties. As such, the robustness of the combined method and its multiple indicators of performance allow the use of intelligent adaptation for improving the performance of the estimation process. For real-time applications, online neural network adaptation may be used to improve the performance by progressively reducing specific modeling uncertainties in the system. In this paper, a new parameter estimation method that uses concepts associated with the EKF, the VSF, and neural network adaptation is introduced. The performance of this method is considered and discussed for applications that involve parameter estimation such as fault detection. [ABSTRACT FROM AUTHOR]

A common problem pertaining to linear or nonlinear systems is the design of a combined robust control and estimation strategy that can effectively deal with noise and uncertainties. The variable structure control (VSC) and its special form of sliding mode control (SMC) demonstrate robustness with regard to uncertainties, although their performance can be severely degraded by noise. As such they can benefit from using state estimates obtained from filters. In this regard, this paper considers the use of a recently proposed robust state and parameter estimation strategy referred to as the variable structure filter (VSF) in conjunction with SMC. The contribution of this paper is a new strategy that combines sliding mode control with the variable structure filter. In the presence of bounded parametric uncertainties and noise, this combined method demonstrates robust stability both in terms of control and state estimation. Furthermore, the combined strategy can be used to achieve high regulation rates or short settling time. The combined VSF and SMC strategy is demonstrated by its application to a high precision hydrostatic system, referred to as the electrohydraulic actuator system. [ABSTRACT FROM AUTHOR]

This paper introduces a reliable strategy for the state of charge (SOC) and the state of health (SOH) estimation of healthy and aged Lithium polymer cells. Dynamics of the cell are modeled using some equivalent circuit models and parameters of each model are calculated by adaptive particle swarm optimization. The modeling process involves modeling and parametric uncertainties as well as measurement and instrumentation noise. They may degrade the performance of an optimal filter for SOC and SOH estimation. To alleviate effects of such uncertain factors, the smooth variable structure filter (SVSF) is implemented. The SVSF is a novel robust state estimation method that benefits from the robustness property of variable structure systems. The performance of the SVSF is compared with the extended Kalman filter (EKF) for real-time SOC estimation of a healthy and an aged Lithium polymer cell. The paper moreover presents a novel method for SOH estimation using the SVSF’s chattering signal and without the need for modeling the cell undergoes aging. Experiments show performance benefits of the SVSF for reliable SOC and SOH estimation of healthy and aged Lithium polymer cells. [ABSTRACT FROM AUTHOR]