Joint Unscented Kalman Filter Research Articles

A model-based tracking control scheme for nonlinear industrial processes involving joint unscented Kalman filter

This paper proposes a model-based reference tracking scheme for stable, MIMO, nonlinear processes. A Joint Unscented Kalman Filtering technique is exploited here to develop a stochastic model of the physical process via simultaneous estimation of the process states and the time-varying/uncertain parameters. Unlike the existing nonlinear model predictive controllers, the proposed scheme does not involve any dynamic optimisation process, which helps to reduce the overall complexity, computation overburden and execution time. Furthermore, the proposed methodology offers robustness to process model-mismatch and considers the effects of stochastic disturbances. A nonlinear two-tank liquid-level control problem and a nonlinear coupled level-temperature control process are studied to demonstrate the usefulness of the proposed scheme.

A Joint Unscented Kalman Filter-Based Dynamic Weigh in Motion System for Railway Vehicles With Traction

Weight of a railway vehicle is a critical parameter which affects the operation of it. The conventional method to monitor the weight of a railway vehicle is based on placing several sensors to the substructure. This substructure based approach does not provide a continuous monitoring. In this study, a vehicle based method, which considers the use of velocity sensors mounted on a vehicle, is proposed for this purpose. It includes the use of a mathematical model of the vehicle and a joint unscented Kalman filter. Methodology has been tested based on the measurements obtained from a full scale wheel on roller type test rig which represents the structure of some trams produced in Czechia with independently rotating wheels. This indirect estimation method reveals that use of models along with a state and parameter filter improves the accuracy of the results. This approach allows continuous monitoring of the weight (or normal load) of a vehicle just with speed sensors. As well as continuous monitoring, this methodology does not require design and implementation of specially designed sensors that should be mounted to vehicle for same purpose.

Multi-phase flow measurement in a gas refinery using decentralized Lyapunov–based adaptive observer

This paper presents a new Lyapunov-based nonlinear adaptive observer and joint unscented Kalman filter to precisely estimate the states and the parameters for the low-order lumped model of the multi-phase flow at the gas refinery. The main focus of the study is to estimate the discharge coefficient of the orifice meter installed on the interconnection lines of the subsystems (parameters) and the total oil and gas mass flows (states). The adaptive estimation is conducted using the real-time measurements including choke pressure, bottom line pressure, single-phase gas flow, and single-phase liquid flow in the refinery outlet. To check the stability and performance of the system against changes, the Lyapunov theory has been used. In all stages, the investigations were based on the data collected from the actual process in the South Pars Gas Complex, Iran. Using the dynamic HYSYS simulation, it is found that the proposed adaptive observer is capable of estimating the oil and gas flows and identifying the discharge coefficient of the flow meter at issue. To show the performance of the proposed adaptive observer, it is evaluated against and compared with the unscented Kalman filter. The comparison of the results obtained from the proposed observer, unscented Kalman filter, and dynamic HYSYS simulation with data collected from the actual process of the refinery shows the appropriate performance of the both estimation algorithms in detecting the changes in liquid and gas flow rates and the consistency of their results with the real process in the South Pars Gas Complex. The simulations reveal that low-order lumped model is sufficient for estimation of parameters and states of the multiphase flow entering the gas refinery.

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter.

It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the time-varying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method.

A Novel and Computationally Efficient Joint Unscented Kalman Filtering Scheme for Parameter Estimation of a Class of Nonlinear Systems

Unscented Kalman filter (UKF) is one type of the sigma point Kalman filters and it is based on unscented transformation. UKF is used for parameter estimation of various dynamic systems and for such purpose either joint UKF (JUKF) or dual UKF (DUKF) schemes are considered. JUKF is based on estimating states and parameters together by using only one filter. For DUKF, states and parameters are decoupled and two separate filters are considered. In this paper, a modification to standard JUKF is proposed for parameter estimation which is based on decoupling parameter vector and updating parameter estimates by considering the error transformation between measurements and transformed sigma points during measurement update into the parameter errors. A linear transformation is proposed for such a purpose. Thus, the computational complexity of the standard JUKF is reduced significantly since parameters are decoupled from the state vector while the convergence of parameter estimate(s) is guaranteed. The new modified JUKF scheme is promising to be used for the parameter estimation of dynamic systems for which a linear transformation between measurement and parameter errors can be obtained. The effectiveness of this new scheme is proven by applying it to two nonlinear dynamic systems.

Reservoir characterization in under-balanced drilling using low-order lumped model

Estimation of the production index of oil and gas from the reservoir into the well during under-balanced drilling (UBD) is studied. This paper compares a Lyapunov-based adaptive observer and a joint unscented Kalman filter (UKF) based on a low order lumped (LOL) model and the joint UKF based on the distributed drift-flux model by using real-time measurements of the choke and the bottom-hole pressures. Using the OLGA simulator, it is found that all adaptive observers are capable of identifying the production constants of gas and liquid from the reservoir into the well, with some differences in performance. The results show that the LOL model is sufficient for the purpose of reservoir characterization during UBD operations. Robustness of the adaptive observers is investigated in case of uncertainties and errors in the reservoir and well parameters of the models.

A new friction condition identification approach for wheel–rail interface

ABSTRACTIn recent years, there has been an increasing interest in designing intelligent vehicles such that they can take necessary actions according to the environmental changes around them and they can inform decision makers about these changes. For safer and cheaper transport, dynamic modelling of these vehicles and identification of such changes in environment based on these models plays an important role. In this study, a sigma point Kalman filter-based scheme (i.e. joint unscented Kalman filter) is proposed to estimate maximum friction coefficient as a parameter in wheel–rail interface. This estimation scheme uses interpretation of lateral and yaw dynamic response of a wheelset to identify maximum friction coefficient. This joint unscented Kalman filter-based approach provides information about the friction conditions in wheel–rail interface without post-processing of estimated data.

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter

In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used.

An Improved Dual Unscented Kalman Filter for State and Parameter Estimation

AbstractA dual unscented Kalman filter (DUKF) is used to estimate the state and the parameter simultaneously via two parallel unscented Kalman filters. The original DUKF usually has performance degradation as a result of assuming the control inputs of each filter are constant, which usually are disturbance inputs or systematic measurement errors in the control system. An improved dual unscented Kalman filter (IDUKF) with random control inputs and sequential dual estimation structure is derived and applicable to the system in which the parameter is linearly observed and uncorrelated with the state. The accuracy, observability, and computational efficiency of the new filter are discussed. Then, the expansibility of the IDUKF for nonlinear parameter observed substructures is investigated. Finally, two simulation experiments about space target tracking and typical time series filtering are shown. The theoretical analyses and simulation results demonstrate the following. (1) the IDUKF can obtain higher accuracy than the original DUKF and a comparative accuracy with the JUKF (joint unscented Kalman filter) when the state and the parameter are not strongly correlated; (2) the IDUKF has better applicability than the DUKF when the state is correlated with the unknown parameter; (3) when the modeling error is not ignorable, the IDUKF is more robust and more accurate than the JUKF due to lower sensitivity to the modeling error.

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

Evaluation of Lyapunov-based Adaptive Observer using Low-Order Lumped Model for Estimation of Production Index in Under-balanced Drilling

A distributed drift-flux model and a low-order lumped model describing a multiphase (gas-liquid) flow in the well during Under-Balanced Drilling (UBD) has been presented. This paper presents a novel nonlinear adaptive observer to estimate the total mass of gas and liquid in the annulus and production constant of gas and liquid from the reservoir into the well during UBD operations. Furthermore, it describes a joint unscented Kalman filter to estimate parameters and states for both the distributed drift-flux and lumped model by using real-time measurements of the choke and the bottom-hole pressures. The performance of the adaptive observers are evaluated for typical drilling scenarios. The results show that all adaptive observers are capable of identifying the production index, although the adaptive observers based on the low-order lumped model achieves better convergence rate than adaptive observer based on the drift-flux model. The results show that the LOL model is sufficient for the purpose of estimating the production parameters.

Joint estimation of states and parameters of Hodgkin–Huxley neuronal model using Kalman filtering

Fitting biophysical models to real noisy data jointly with extracting fundamental biophysical parameters has recently stimulated tremendous studies in computational neuroscience. Hodgkin–Huxley (HH) neuronal model has been considered as the most detailed biophysical model for representing the dynamical behavior of the spiking neurons. The unscented Kalman filter (UKF) has been already applied to track the dynamics of the HH model. In this paper, we extend the existing Kalman filtering (KF) technique for the HH model to another version, namely, extended Kalman filtering (EKF). Two estimation strategies of the KF, dual and joint estimation strategies, are derived for simultaneously tracking the hidden dynamics and estimating the unknown parameters of a single neuron, leading to four KF algorithms, namely, joint UKF (JUKF), dual UKF (DUKF), joint EKF (JEKF) and dual EKF (DEKF). Detailed derivations of these four methods and intensive simulation studies are provided. Our main contribution is in the derivation of the EKF-based methods, JEKF and DEKF, for tracking the states and estimating the parameters of the HH neuronal model. Since these EKF-based methods are faster than UKF-based versions, they can particularly be employed in dynamic clamp technique, which connects artificial and biological neurons in order to assess the function of neuronal circuits.

Joint estimation in batch culture by using unscented kalman filter

The disturbances caused by uncertain factors are inevitable in microbial fermentation. In this paper, we study the joint estimation problem for state and parameter in the bio-dissimulation process of glycerol to 1,3-PD in batch culture. Based on the nonlinear stochastic dynamic system model, we establish the corresponding iteration equations of Joint Unscented Kalman Filter (UKF) by referring to the Extended Kalman Filter (EKF), which is generally applied in microbial fermentation. Through numerical computation, both the state estimations and the uncertain model parameter estimations are obtained. Furthermore, the results of different parameter identification methods are compared. The results show that Joint UKF is more feasible for the process of controlling the glycerol fermentation.