Implementation Of Unscented Kalman Filter Research Articles

Kronecker Product Based Modeling of Darlington Amplifier and State Estimation using Unscented Kalman Filter

ABSTRACT This paper presents the application of unscented Kalman filter (UKF) for output voltage estimation of the Darlington amplifier (DA). Implementation of UKF requires the state-space model of DA circuit, which has been obtained using Kirchhoff’s voltage law (KVL), Kirchhoff’s current law (KCL) and the Gummel–Poon model of the bipolar junction transistor (BJT). This work uses Kronecker product for vector multiplication. The mathematical representation of nonlinear system in terms of Kronecker product helps to reduce the computational burden involved in the vector multiplication. The proposed method has the advantage that it can be used for the nonlinear region and large amplitude input signal. As the maximal precision of estimation requires modelling in terms of circuit elements and device parameters, the proposed method is able to provide an accurate estimation. We compared the UKF estimation results with the extended Kalman filter (EKF) and iterated extended Kalman filter (IEKF). The UKF presents smaller mean square error (MSE) as compared to EKF and IEKF as UKF is accurate to the third order for any nonlinearity. In addition, IEKF presents smaller MSE as compared to EKF because the linearisation error is taken into account by IEKF.

State-Feedback-Critical Super Twisting Sliding Mode Control for a Half-Car Suspension System

A good suspension system is of paramount importance to the operating performance of a vehicle and, consequently, to the safety and driving comfort of the passengers. Nevertheless, suspension systems are commonly susceptible to nonlinearity, parameter uncertainty, and exogenous perturbation, which can easily impair their effectiveness. This study first employs a full state feedback super twisting control (FS-STC) to stabilize both vertical displacement and pitch angle of a half-car suspension system in the presence of disturbances. FS-STC inherits the robust property of sliding mode control (SMC) while effectively attenuating the chattering phenomenon as one of its attractive features. However, FS-STC strictly requires both direct displacement and velocity state feedback, which implies additional sensors have to be installed, thus increasing the complexity of the physical structure and being prone to measurement noises. Therefore, a higher order sliding mode observer (HOSMO) based STC (HOSMO-STC) and an unscented Kalman filter (UKF) based STC (UKF-STC) are subsequently proposed to tackle this state availability problem. HOSMO estimates velocity states, thus reducing the dependence on state feedback for STC design. Meanwhile, UKF implementation takes further actions by utilizing more common and easily accessible relative displacements such as suspension strokes to estimate all concerned system states. Comparative simulation results demonstrate that UKF-STC offers better performance in terms of both convergence accuracy and chattering alleviation compared to FS-STC and HOSMO-STC while requiring the least information of state feedback.

State Estimation of RC Filters using Unscented Kalman Filter

This paper implements unscented Kalman filter (UKF) for output voltage estimation of RC low pass filter (LPF) and high pass filter (HPF). At first, the state space model has been obtained using Kirchhoff’s current law (KCL). The performance of UKF has been compared with extended Kalman filter (EKF). The simulation results validate the superiority of UKF over EKF as the estimation error is smaller using UKF as compared to the EKF method. As the UKF uses unscented transform (UT) and EKF uses Taylor series expansion for linearization purpose, linearization error is smaller in UKF as compared to EKF method. Also, UKF implementation has the advantage that it does not require Jacobian computation of nonlinear system model.

Identification of an experimental nonlinear energy sink device using the unscented Kalman filter

Abstract Nonlinear energy sink (NES) devices have recently been introduced as a means of passive structural control and have been shown to effectively dissipate energy from structural systems during extreme vibrations. Due to their essential geometric nonlinearities, time domain based methods are often applied for identifying their system parameters, which is a challenging task. The unscented Kalman filter (UKF) has been shown in numerical studies to be robust to highly nonlinear systems with noisy data and therefore presents a promising option for identification. In this study, the UKF is used to determine the model parameters of an experimental NES device whose behavior is governed by a geometric nonlinearity in its stiffness and a friction-based nonlinearity in its damping. The standard implementation of the UKF is compared with two implementation methods developed by the authors, which vary in their use of experimental responses to train the NES device model. The impact of choosing different prior distributions on the parameters is also analyzed through Latin hypercube sampling to enhance the quality of the identification for practical implementation, where the prior distribution on the parameters is often ill-defined. The identified models generated using one of the proposed UKF implementation methods is shown to provide a robust model of the NES, demonstrating that the UKF can be used for parameter identification with this class of devices.

Sensorless control of permanent magnet synchronous motor drive using unscented Kalman filter

This paper deals with the modelling and implementation of unscented Kalman filter (UKF) for estimation of rotor speed and position in a vector controlled PMSM drive. While with conventional control methods, the transient operation is unsatisfactory, vector control of the PMSM drive offers higher dynamic performance. UKF is quite effective for nonlinear systems as it applies unscented transformation (UT) to nonlinear systems directly without linearising the system. Also, in this paper, speed control of the drive is achieved using integral sliding mode controller (ISMC). The use of ISMC along with the UKF-based observer reduces the tuning burden of sensorless control scheme. Combining the UKF and ISMC techniques, the dynamic performance of the PMSM drive is improved irrespective of the nonlinearities and disturbances in the system. Simulation of a PMSM drive is carried out in MATLAB/Simulink subject to disturbances for varying load and speed in order to verify the performance of the drive.

Tracking neuronal dynamics during seizures

We demonstrate that a meaningful estimation of the neuronal network dynamics from single measured variable is now possible by incorporating biophysical neuronal models into a model-based control framework. Specifically, we assimilate noisy membrane potential (V) measurements from individual hippocampal neurons to reconstruct the dynamics of networks of these cells, their extracellular microenvironment, and the activities of different neuronal types during seizures. We used two-compartmental models for the pyramidal cells (PCs) and interneurons (INs): a cellular compartment and the surrounding extracellular microenvironment. The membrane potentials of both cells were modeled by Hodgkin-Huxley equations containing sodium, potassium, calcium-gated potassium, and leak currents. The current equations were augmented with dynamic variables representing the intra- and extracellular ion concentrations (K + , Na + ,a nd Ca 2+ ). To estimate and track the dynamics of the neuronal networks, we applied a nonlinear ensemble version of the Kalman filter, the Unscented Kalman Filter (UKF) [1]. Details of the model and UKF implementation can be found in [2,3]. In Fig. 1 we show an example where we use V measurements to estimate the rest of the dynamics of CA1 PC. Fig. 1A shows an intracellular recording from a PC during seizures, and plot the estimated [K]o in Fig. 1B.

Multisensor-Based Human Detection and Tracking for Mobile Service Robots

One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.