Mohammad Al-Shabi

Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy

In this paper, nonlinear state estimation problems with modeling uncertainties are considered. As demonstrated recently in literature, the cubature Kalman filter (CKF) provides the closest known approximation to the Bayesian filter in the sense of preserving second-order information contained in noisy measurements under the Gaussian assumption. The smooth variable structure filter (SVSF) has also been recently introduced and has been shown to be robust to modeling uncertainties. In an effort to utilize the accuracy of the CKF and the robustness of the SVSF, the CKF and SVSF have been combined resulting in an algorithm referred to as the CK-SVSF. The robustness and accuracy of the CK-SVSF was validated by testing it on two different computer problems, namely, a target tracking problem and the estimation of the effective bulk modulus in an electrohydrostatic actuator.

Kalman filtering strategies utilizing the chattering effects of the smooth variable structure filter

The Kalman filter (KF) remains the most popular method for linear state and parameter estimation. Various forms of the KF have been created to handle nonlinear estimation problems, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The robustness and stability of the EKF and UKF can be improved by combining it with the recently proposed smooth variable structure filter (SVSF) concept. The SVSF is a predictor-corrector method based on sliding mode concepts, where the gain is calculated based on a switching surface. A phenomenon known as chattering is present in the SVSF, which may be used to determine changes in the system. In this paper, the concept of SVSF chattering is introduced and explained, and is used to determine the presence of modeling uncertainties. This knowledge is used to create combined filtering strategies in an effort to improve the overall accuracy and stability of the estimates. Simulations are performed to compare and demonstrate the accuracy, robustness, and stability of the Kalman-based filters and their combinations with the SVSF.

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.

The Unscented Smooth Variable Structure Filter Application Into a Robotic Arm

Robotic arms are becoming increasingly popular in industrial applications. However, improving the response and accuracy of robotic arms while reducing their cost has become challenging. The Kalman Filter (KF) has attracted a significant amount of research as it improves the control quality by filtering the feedback signal. On the other hand, KF solution becomes very challenging when the system under study is nonlinear. This work proposes a new online state estimation algorithm that combines the Smooth Variable Structure Filter (SVSF) with the Unscented Kalman Filter (UKF). The proposed method overcomes the limitations of SVSF and UKF in terms of stability and sensitivity to noise. A simulation study is conducted in this paper to demonstrate the results of the proposed method when applied to estimate the states of a PRRR industrial robotic arm.Copyright

UAV dynamics model parameters estimation techniques: A comparison study

Unmanned Aerial Vehicles (UAVs) dynamics modeling and parameter estimation has recently occupied great interest due to their vast use in military, civilian, industrial and agricultural applications. Accurate Online UAV parameter estimation is essential for robust autonomous control design. This study presents two different online UAV parameter estimation methods; the smooth variable structure filter (SVSF) method and the recursive least squares (RLS) method. This work presents the application of the SVSF method; including chattering signals information which previously was applied to linear models, over a nonlinear dynamics model. Moreover, the work presents a simulation study to assess the performance of the methods in terms of accuracy and speed of convergence when applied to estimate the parameters of a general Quadrotor dynamics model. The better method might be considered for deployment in an experimental UAV parameter estimation project under run by the authors.

The sigma-point central difference smooth variable structure filter application into a robotic arm

Recent Mobile-robots/Robotic-manipulators based industrial applications require accurate control despite the blurry and the noisy feedback signals. As a result, there is an increasing demand for new estimation techniques and filters to overcome accompanying system disturbances especially when nonlinearity present in the system. Industrial applications control quality will improve if a robust filter is used to reduce the effect of noise and to improve the quality of feedback signals by handling those nonlinearities. In this work, a new filter that combines the Smooth Variable Structure Filter (SVSF) with the Central Difference Kalman Filter (CDKF) is proposed. The presented method results in robust, stable and accurate estimation algorithm for motion states which are measured to be feedback signals. Results are demonstrated by applying the proposed filter to estimate the states of a 4-axis industrial robot arm with one Prismatic, and three Rotational joints (PRRR).

A fuzzy-smooth variable structure filtering strategy: For state and parameter estimation

This article introduces a new filtering strategy based on combining elements of fuzzy logic and the smooth variable structure filter (SVSF). A revised formulation of the SVSF is presented in an effort to combine it with fuzzy logic, and is referred to as the RSVSF. Computer simulations are used to compare the new strategy, referred to as the Fuzzy-SVSF, with other popular Kalman-based estimation methods. Preliminary results indicate that combining fuzzy logic with the SVSF yields an improved estimation result and improved stability to system and modeling changes and uncertainties.

The Toeplitz-observability smooth variable structure filter

The smooth variable structure filter (SVSF) is a recently proposed method that is used for estimation purposes, such as fault detection [1-2]. The SVSF demonstrates good results and robustness when it is applied to linear and nonlinear systems that are fully measured. However, the results differ when some of the states are not measured. In this case, the SVSF is combined with the Luenberger method, which has some limitations. In this paper, a novel form of the SVSF is derived using the Observability and Toeplitz matrices. The benefits of the proposed method are demonstrated by using a computer simulation that involves an electro-hydrostatic actuator proposed in [3-5].

Smooth Variable Structure Filter for pneumatic system identification

The Smooth Variable Structure Filter (SVSF) is a newly-developed predictor-corrector filter for state and parameter estimation [1]. The SVSF is based on the Sliding Mode Control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is suitable for fault detection and identification applications because of its stability and robustness in modeling uncertainties. The SVSF has two indicators of performance; the a posteriori output error and the chattering. The latter — as a signal-contains the systems information which is proven and explored in this paper. The SVSF is applied for the identification of pneumatic systems in order to verify the proposed method. Furthermore, the proposed method is compared with neural network and the results reveal that SVSF is better in identifying nonlinear systems.

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.