Showing papers in "Journal of Process Control in 2013"

TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.

TL;DR: This paper provides a general introduction to the main steps involved in development and implementation of industrial inferential sensors, and presents an overview of the relevant Bayesian methods for inferential modeling.

TL;DR: The process models with time delay mainly adopted for identification in the literature are presented with a classification on different response types, along with two specific categories for robust identification against load disturbance and the identification of multivariable or nonlinear processes.

TL;DR: An integrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-grade processes and the superiority of the proposed soft sensor is compared with other soft sensors in terms of online prediction of melt index in an industrial plant in Taiwan.

TL;DR: In this paper, a nonlinear power plant dynamic is described by a fuzzy model which contains local liner models, and the resulting NMPILC is constituted based on this fuzzy model.

TL;DR: In this paper, the authors consider PI controller tuning for the Integral Plus Dead Time (IPDT) plant subject to constraints on tolerable deviations from ideal shapes and guaranteeing minimal combined IAE (Integral of Absolute Error) measure composed of weighted IAE values of the setpoint and disturbance step responses.

TL;DR: In this article, a robust predictive control approach for additive discrete time uncertain nonlinear systems is proposed, where the controller design is characterized as an optimization problem of the "worst-case" objective function over an infinite moving horizon.

TL;DR: In this paper, the authors address the model-based tuning of single-loop PID controllers in terms of the robustness/performance and servo/regulator trade-offs.

TL;DR: In this paper, the optimal nominal tuning of a new modification of predictive disturbance observer based filtered PI control (PDO FPI) applied to a first order plus dead time (FOPDT) plant with exactly known parameters is analyzed.

TL;DR: In this paper, the authors developed and deployed an advanced model-based control framework for a polymer electrolyte membrane (PEM) fuel cell system using a reliable and efficient dynamic optimization approach which discretizes both manipulated and state variables.

TL;DR: A parallel coordinate descent algorithm for solving smooth convex optimization problems with separable constraints that may arise, e.g. in distributed model predictive control (MPC) for linear network systems, which has low iteration complexity and is suitable for distributed implementations.

TL;DR: The aim of the work presented in this paper is to reduce the number of alerts presented to the operator with an effective alarm management strategy that can help plant owners and operators to comply with standards for alarm management.

TL;DR: In this paper, a novel approach to this problem involves applying dimensional analysis theory to obtain a generalized model of the control loop and then to perform a parameter tuning for its dimensionless representation.

TL;DR: In this paper, the authors proposed a gain scheduling approach based on considering that the LPV system, scheduling parameters and their derivatives with respect to time lie in a priori given hyper rectangles.

TL;DR: An aggregated k-means algorithm produces an optimal ensemble clustering solution for a multimode process dataset that enables the development of a single principal component analysis (PCA) model for processes operating under multiple desired steady-states.

TL;DR: In this article, a model quality index is developed by using the ratio of a quadratic form of model residuals and that of the estimated disturbance innovations from closed-loop data.

TL;DR: In this paper, a fault diagnosis method is developed for a particular class of nonlinear systems described by a polytopic Linear Parameter Varying (LPV) formulation, which consists in the synthesis of an accurate Fault Detection and Isolation (FDI) filter and also a sensor fault magnitude estimation with a quality factor.

TL;DR: A probability of being steady or at leas t stationary over the window is computed by performing a residual Student-t test using the estimated mean of the process signal without any drift and the estimated standard-deviation of the underlying white-noise driving force.

TL;DR: In this paper, a discrete-time robust nonlinear filtering algorithm is proposed to deal with the contami-nated Gaussian noise in the measurement, which is based on a robust modification of the derivative-free Kalman filter.

TL;DR: In this article, a distributed moving horizon state estimation (DMHE) design for a class of nonlinear systems with bounded output measurement noise and process disturbances is proposed, and the performance of the proposed DMHE is illustrated via the application to a reactor-separator process example.

TL;DR: In this paper, the problem of reliable passive control for singular systems with time-varying delays is investigated, and a delay-dependent condition is established to guarantee the considered system to be regular, impulse-free, exponentially stable, and passive.

TL;DR: In this article, three nonlinear MPC algorithms for neural Wiener models are described for two nonlinear processes: a polymerization reactor and a neutralization reactor. But none of the discussed algorithms do not need an inverse of the steady-state part of the model.

TL;DR: In this article, the authors proposed a delay-range-dependent output feedback controller combined with iterative learning control (ILC) for batch processes with uncertain perturbations and interval time-varying delays, where the main idea is to transform the design into a robust delayrangedependent H∞ control of a 2D system described by a state-space model with varying delays.

TL;DR: In this article, a general structure to control long time-delay plants is proposed and an easy methodology to tune the control parameters is outlined, where all the sensitivity transfer functions are delay free.

TL;DR: This work proposes a generalized delay-timer framework where instead of consecutive n samples in the conventional case, n 1 out of n consecutive samples are considered to raise an alarm and three important performance indices, namely, the false alarm rate, the missed alarm rate and the expected detection delay are calculated.

TL;DR: In this paper, a robust tuning method for two-degree-of-freedom (2DoF) proportional integral derivative controllers with filter (PID2F) for inverse response controlled processes modeled by a second-order plus a right-half plane zero (SOPRHPZ) transfer function is presented.

TL;DR: This paper proposes a fast NMPC method based on NLP sensitivity, called advanced-multi-step NMPC (amsNMPC), and two variants of this method are developed, the parallel approach and the serial approach.

TL;DR: A new recursive algorithm is proposed for the identification of a special form of Hammerstein–Wiener system with dead-zone nonlinearity input block to implement on-line control strategies on this kind of system to produce adaptive control algorithms.

TL;DR: In this article, a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation is presented, where the global system is decomposed into several subsystems.

TL;DR: In this paper, a multi-model approach is developed, wherein a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system.