A novel model predictive control with state estimator is proposed for polytopic uncertain systems with actuator saturation in which the actuator saturation is described as a convex hull composition. At each sampling time, a state feedback control law is obtained such that the upper bound of the quadratic objective function is minimized. The intrinsic relationship between infinite horizon contro...

A lexical transducer, first discussed in Karttunen, Kaplan and Zaenen 1992, is a specialised finite-state automaton that maps inflected surface forms to lexical forms, and vice versa. The lexical form consists of a canonical representation of the word and a sequence of tags that show the morphological characteristics of the form in question and its syntactic category. For example, a lexical tra...

The problem considered here is state estimation in the presence of bounded process and measurement noise. A new nonlinear state estimator, based on interval analysis and the notion of set inversion, is applied to robot localization and tracking. This estimator evaluates a set guaranteed to contain all values of the state that are consistent with the available observations, given the noise bound...

We present a new method for obtaining confidence intervals in steady-state simulation. In our replicated batch means method, we do a small number of independent replications to estimate the steady-state mean of the underlying stochastic process. In order to obtain a variance estimator, we further group the observations from these replications into nonoverlapping batches. We show that for large ...

This paper presents a comprehensive formulation of a linearized state space process model for a generic two-reactant-two-product reactive distillation system. The development of the model requires the knowledge of the desired steady state design data, including liquid holdups and composition profiles. The application of the developed linear process model in composition estimation and control of...

The Kalman filter is the optimal minimum-variance state estimator for linear dynamic systems with Gaussian noise. In addition, the Kalman filter is the optimal linear state estimator for linear dynamic systems with non-Gaussian noise. For nonlinear systems various modifications of the Kalman filter (e.g., the extended Kalman filter, the unscented Kalman filter, and the particle filter) have bee...

pH control is a challenging problem due to its highly nonlinear nature. In this paper the performances of two different adaptive global linearizing controllers (GLC) are compared. Least squares technique has been used for identifying the titration curve. The first controller is a standard GLC based on material balances of each species. For implementation of this controller a nonlinear state...

In this paper, a cascade discrete-continuous state estimator on a partial order is proposed and its existence investigated. The continuous state estimation error is bounded by a monotonically nonincreasing function of the discrete state estimation error, with both the estimation errors converging to zero. This work shows that the lattice approach to estimation is general as the proposed estimat...