Receive a 20% Discount on All Purchases Directly Through IGI Global's Online Bookstore

Majdi Mansouri (Texas A&M University at Qatar, Qatar), Moustafa Mohamed-Seghir (Gdynia Maritime University, Poland), Hazem Nounou (Texas A&M University at Qatar, Qatar), Mohamed Nounou (Texas A&M University at Qatar, Qatar) and Haitham A. Abu-Rub (Texas A&M University at Qatar, Qatar)

Source Title: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications

Copyright: © 2014
|Pages: 38
DOI: 10.4018/978-1-4666-4450-2.ch024

Chapter Preview

TopMany process operations, such as modeling, monitoring, and control, require the availability of state and/or parameter measurements. However, due to the difficulty of, or cost associated with, obtaining these measurements, state and/or parameter estimators are often used to overcome this problem. For example, in process monitoring and control, sometimes it is challenging to measure some of the key variables. In such cases, estimates of these variables can be obtained using state estimation. Also, in modeling, several model parameters need to be estimated. Estimating these parameters usually requires several experimental setups that can be challenging and expensive. Hence, estimating such parameters using state estimation can be of a great value. In this chapter, the objective is to compare the performances of various state-of-the-art state estimation techniques in estimating the state variables using different kinds of observation models (i.e., biological model and power system) and their abilities to estimate some of the key system parameters, which are needed to define the process model. Several estimation techniques, such as the extended Kalman filter, unscented Kalman filter and more recently the Sequential Monte Carlo method have been developed and utilized in many applications. Several estimation techniques, such as the extended Kalman filter, unscented Kalman filter and more recently the Sequential Monte Carlo method have been developed and utilized in many applications. The classical Kalman Filter (KF) was developed in the 1960s (Kalman, 1960), and has been widely applied in various engineering and science areas, including communications, control, machine learning, neuroscience, and many others. In the case where the model describing the system is assumed to be linear and Gaussian, the KF provides an optimal solution (Simon, 2006; Grewal & Andrews, 2008). KF has also been formulated in the context of Takagi-Sugeno fuzzy systems, which can be described by a convex set of multiple linear models (Chen et al., 1998; Simon, 2003). It is known that KF is computationally efficient; however, it is limited by the non-universal linear and Gaussian modeling assumptions. To relax such assumptions, the Extended Kalman Filter (Simon, 2006; Grewal & Andrews, 2008; Julier et al., 1997; Ljung et al., 1979; Kim et al., 1994) and the Unscented Kalman Filter (Simon, 2006; Grewal & Andrews, 2008; Wan et al., 2000; Wan et al., 2001; Sarkka et al., 2001; Sarkka et al., 2007) have been developed. In extended Kalman filtering, the model describing the system is linearized at every time sample (which means that the model is assumed to be differentiable). Therefore, for highly nonlinear models, EKF does not usually provide a satisfactory performance. The UKF, on the other hand, instead of linearizing the model to approximate the mean and covariance matrix of the state vector, uses the unscented transformation to approximate these moments. In the unscented transformation, a set of samples (called sigma points) are selected and propagated through the nonlinear model to improve the approximation of these moments and thus the accuracy of state estimation. Other state estimation techniques use a Bayesian framework to estimate the state and/or parameter vector (Beal et al., 2003). The Bayesian framework relies on computing the probability distribution of the unobserved state given a sequence of the observed data in addition to the state evolution model. Consider an observed data set y, which is generated from a model defined by a set of unknown parameters (Smidl et al., 2005). The beliefs about the data are completely expressed via the parametric probabilistic observation model, The learning of uncertainty or randomness of a process is solved by constructing a distribution called the posterior distribution, which quantifies our belief about the system after obtaining the measurements. According to Bayes rule, the posterior can be expressed as

Cad System: Cad system is one of the conditional stress response modules in E.coli, which is induced only at low pH and a lysine-rich environment

Induction Motor (IM): Induction motor is an AC motor in which all electromagnetic energy is transferred by inductive coupling from a primary coiling to a secondary coiling, where the two coilings are separated by an air gap. In three-phase induction motors, energy is transferred from the stator to either a wound rotor or a short-circuited squirrel cage rotor.

Power System: Power system is a system of high tension cables by which electrical power is distributed throughout a region. It is also known as a network of electrical components used to supply, transmit and use electric power.

Unscented Kalman filter (UKF): UKF is an estimation technique that uses a deterministic sampling technique known as the unscented transform to approximate the mean and the covariance of the state and parameter vector.

Extended Kalman Filter (EKF): EKF is an estimation technique that is applicable to nonlinear and non-Gaussian models. As the Kalman filter is applicable to linear systems, the EKF can be viewed as an extension of the Kalman filter that is applied to a linearized version of the nonlinear model.

Particle Filter (PF): PF, which is based on Bayesian estimation, is a sequential Monte Carlo state estimation method for nonlinear and non-Gaussian systems. The EKF and UKF algorithms do not always provide a satisfactory performance, especially for highly nonlinear processes as model linearization does not necessarily provide good estimates of the mean of the state vector and the covariance matrix of the estimation error, which are used in state estimation. These issues are addressed by the PF.

Biological System: Biological system is an organ or group of organs within the body that work collectively to achieve a certain task.

States and Parameters Estimation: State and parameter estimation is the process of obtaining the estimators of state variables and model parameters based on a dataset.

Search this Book:

Reset

Copyright © 1988-2018, IGI Global - All Rights Reserved