The Experts below are selected from a list of 549153 Experts worldwide ranked by ideXlab platform
Lorenz T Biegler - One of the best experts on this subject based on the ideXlab platform.
-
advanced multi step Nonlinear Model predictive control
Journal of Process Control, 2013Co-Authors: Xue Yang, Lorenz T BieglerAbstract:Abstract Nonlinear Model predictive control (NMPC) has gained widespread attention due to its ability to handle variable bounds and deal with multi-input, multi-output systems. However, it is susceptible to computational delay, especially when the solution time of the Nonlinear programming (NLP) problem exceeds the sampling time. In this paper we propose a fast NMPC method based on NLP sensitivity, called advanced-multi-step NMPC (amsNMPC). Two variants of this method are developed, the parallel approach and the serial approach. For the amsNMPC method, NLP problems are solved in background multiple sampling times in advance, and manipulated variables are updated on-line when the actual states are available. We present case studies about a continuous stirred tank reactor (CSTR) and a distillation column to show the performance of amsNMPC. Nominal stability properties are also analyzed.
-
advanced multi step Nonlinear Model predictive control
IFAC Proceedings Volumes, 2012Co-Authors: Xue Yang, Lorenz T BieglerAbstract:Abstract Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on Nonlinear programming (NLP) and NLP sensitivity. This method includes two approaches: the serial approach and the parallel approach. These two approaches solve the background Nonlinear programming (NLP) problem at different frequencies and update manipulated variables within each sampling time using NLP sensitivity. We present a continuous stirred tank reactor (CSTR) example to demonstrate the performance of amsNMPC and analyze the results.
Xue Yang - One of the best experts on this subject based on the ideXlab platform.
-
advanced multi step Nonlinear Model predictive control
Journal of Process Control, 2013Co-Authors: Xue Yang, Lorenz T BieglerAbstract:Abstract Nonlinear Model predictive control (NMPC) has gained widespread attention due to its ability to handle variable bounds and deal with multi-input, multi-output systems. However, it is susceptible to computational delay, especially when the solution time of the Nonlinear programming (NLP) problem exceeds the sampling time. In this paper we propose a fast NMPC method based on NLP sensitivity, called advanced-multi-step NMPC (amsNMPC). Two variants of this method are developed, the parallel approach and the serial approach. For the amsNMPC method, NLP problems are solved in background multiple sampling times in advance, and manipulated variables are updated on-line when the actual states are available. We present case studies about a continuous stirred tank reactor (CSTR) and a distillation column to show the performance of amsNMPC. Nominal stability properties are also analyzed.
-
advanced multi step Nonlinear Model predictive control
IFAC Proceedings Volumes, 2012Co-Authors: Xue Yang, Lorenz T BieglerAbstract:Abstract Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on Nonlinear programming (NLP) and NLP sensitivity. This method includes two approaches: the serial approach and the parallel approach. These two approaches solve the background Nonlinear programming (NLP) problem at different frequencies and update manipulated variables within each sampling time using NLP sensitivity. We present a continuous stirred tank reactor (CSTR) example to demonstrate the performance of amsNMPC and analyze the results.
R.k. Pearson - One of the best experts on this subject based on the ideXlab platform.
-
Selecting Nonlinear Model structures for computer control
Journal of Process Control, 2003Co-Authors: R.k. PearsonAbstract:Many authors have noted the difficulty of developing the Models required for Nonlinear Model predictive control (NMPC) and other Nonlinear, Model-based control strategies. One reason this task is difficult is that success depends strongly on initially selecting a reasonable structure for this Nonlinear Model. Unfortunately, this selection is extremely difficult because most of our intuition about structure/behavior relations (e.g., if the step response exhibits overshoot, a Model of at least second order is required) is based on experience with relatively low-order linear Models and often fails completely when confronted with comparably simple Nonlinear Models. To help bridge this chasm between Nonlinear Model behavior and our linear intuition, this paper describes some broad classes of Nonlinear Model structures, which may be approximately characterized as mildly Nonlinear, strongly Nonlinear, or of intermediate Nonlinearity, depending on the different ways they violate linear intuition. It is hoped that these results will be useful in selecting simple Nonlinear Model structures for use in Model-based control.
-
Practically-motivated input sequences for Nonlinear Model identification
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998Co-Authors: R.k. Pearson, Patrick H. Menold, Frank AllgöwerAbstract:We consider the use of binary sequences for the identification of simple Nonlinear dynamic Models. The results presented illustrate the nature of the interplay between the structure of the Nonlinear Model, the switching probability of the binary sequence, and the variability of the resulting parameter estimates. Extension to MIMO problems are proposed.
Hadi Davilu - One of the best experts on this subject based on the ideXlab platform.
-
robust Nonlinear Model predictive control for a pwr nuclear power plant
Progress in Nuclear Energy, 2012Co-Authors: H Eliasi, Mohammad Bagher Menhaj, Hadi DaviluAbstract:Abstract One of the important operations in nuclear power plants is power control during load following in which many robust constraints on both input and measured variables must be satisfied. This paper proposes a robust Nonlinear Model predictive control for the load-following operation problem by considering some robust constraints on both input and output variables. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is quite effective while the constraints are robustly kept satisfied.
Cesar De Prada - One of the best experts on this subject based on the ideXlab platform.
-
improving scenario decomposition algorithms for robust Nonlinear Model predictive control
Computers & Chemical Engineering, 2015Co-Authors: Ruben Marti, Sergio Lucia, D Sarabia, Radoslav Paulen, Sebastian Engell, Cesar De PradaAbstract:Abstract This paper deals with the efficient computation of solutions of robust Nonlinear Model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust Nonlinear Model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two Nonlinear case studies.
