In this article, we propose an optimization based formulation for design of optimal inputs for multivariate systems. A well designed identification experiment can generate good quality models while incurring significant costs. In a typical process plant, the nominal policy is to operate the plant at or near constraints to achieve economic benefits. In this work, we quantify the cost of the experiment carried out on such systems in terms of the deviation from the nominal operational policy. The objective is to minimize the cost incurred during the experiment without violating the operational constraints while guaranteeing model quality. The proposed economics based optimization formulation is non-convex and hence we present a twostep iterative algorithm. The inputs are realized as white noise sequence filtered through an M-tap multivariate FIR filter. The filter coeffcients are obtained by the spectral factorization. A detailed simulation study is presented to illustrate the proposed approach. © 2015

Abhishankar Kumar

M. Nabil

Sridharakumar Narasimhan

Convex optimization

Costs

Economics

FIR filters

Identification (control systems)

Iterative methods

INPUT DESIGN

Iterative algorithm

MULTIVARIATE SYSTEMS

Operational constraints

OPERATIONAL POLICY

Optimization formulations

SPECTRAL FACTORIZATIONS

WHITE NOISE SEQUENCE

White noise

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

IFAC-PapersOnLine

Input design is the process of designing an informative input that extracts maximal information about system dynamics using minimal cost and resources. In this work, we design inputs in the economic framework, where the objective is to minimize the cost incurred during the experiment, subject to plant friendly constraints as well as feasibility constraints. The cost is quantified in terms of departure from the nominally optimal operational policy. Typically, the nominal policy is to operate the plant at or near constraints. The economics based optimization formulation is non-convex, so we have proposed a 2-step iterative method to solve this problem. The solution also yields the conditions at which the identification experiment has to be carried out. The input is realized as white noise filtered by an M-tap FIR filter. © 2014 EUCA.

2014 European Control Conference, ECC 2014

Economical and plant friendly input design for system identification

The operating point of a typical chemical process is determined by solving a non-linear optimization problem where the objective is to minimize an economic cost subject to constraints. Often, some or all of the constraints at the optimal solution are active, i.e., the solution is constrained. Though it is profitable to operate at the constrained optimal point, it might lead to infeasible operation due to uncertainties. Hence, industries try to operate the plant close to the optimal point by "backing-off" to achieve the desired economic benefits. Therefore, the primary focus of this paper is to present an optimization formulation for solving the dynamic back-off problem based on an economic cost function. In this regard, we work within a stochastic framework that ensures feasible dynamic operating region within the prescribed confidence limit. In this work, we aim to reduce the economic loss due to the back-off by simultaneously solving for the operating point and a compatible controller that ensures feasibility. Since the resulting formulation is non-linear and non-convex, we propose a novel two-stage iterative solution procedure such that a convex problem is solved at each step in the iteration. Finally, the proposed approach is demonstrated using case studies. © 2014 Elsevier Ltd.

Journal of Process Control

Profitable and dynamically feasible operating point selection for constrained processes

This paper discusses the minimum backed of operating point selection problem based on process economics. In this work, we consider the case where the nominal operating point is not completely constrained, i.e., there are some unconstrained degrees of freedom or manipulations available. In this regard, we propose a stochastic formulation that ensures feasible dynamic operating region within the prescribed confidence limit. Furthermore, the formulation also finds a suitable multivariable controller to achieve economic benefits. The problem is nonlinear and non-convex and hence an iterative solution procedure is proposed such that at each step in the iteration, a convex problem is solved. Finally, the approach is illustrated using an evaporation process. © 2012 IFAC.

IFAC Proceedings Volumes (IFAC-PapersOnline)

Economic back-off selection based on optimal multivariable controller

Cambridge University Press

Convex Optimization

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference

Fundamental limitations on the accuracy of MIMO linear models obtained by PEM for systems operating in open loop

Economical Input Design for Identification of Multivariate Systems

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Publisher	Data powered by TypesetElsevier B.V.
ISSN	24058963
Open Access	Yes