A gap metric based multiple model approach for nonlinear switched systems

Hariprasad, K.; Bhartiya, Sharad; Gudi, Ravindra D.

doi:10.1016/j.jprocont.2012.07.005

Cited by 41 publications

(20 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Perhaps, the most common type of empirical model is a linear model. When a process system exhibits significant nonlinearities as is the case in most chemical processes, the use of multiple linear models has been employed to improve the accuracy of prediction over a larger operating region [38,39,36]. One potential grouping of the various methods of system identification is to group the methods on the basis of the type of empirical model derived which may be either an input-output model or a state-space model.…”

Section: Introductionmentioning

confidence: 99%

Economic model predictive control of nonlinear process systems using empirical models

2014

View full text Add to dashboard Cite

Economic model predictive control (EMPC) is a feedback control technique that attemptsto tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. ii The thesis of Anas Wael Alanqar is approved.

show abstract

Section: Introductionmentioning

confidence: 99%

Economic model predictive control of nonlinear process systems using empirical models

2014

View full text Add to dashboard Cite

show abstract

“…More recently, they were re-discussed by Jin et al (2011). This framework is also available for hybrid (Nandola & Bhartiya, 2008) and switched (Hariprasad et al, 2012) nonlinear systems and could be adapted for the (hybrid, switched) LPV case. Evidently, this topic is rather under-explored.…”

Section: Multiple Model Mpcmentioning

confidence: 99%

Model predictive control design for linear parameter varying systems: A survey

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

128

View full text Add to dashboard Cite

Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.

show abstract

“…Also, the combination problem is how to obtain a global controller according to the designed local controllers to meet the aims such as stability and performance (Ahmadi et al, 2019). The combination problem could be classified into soft-switching method (Du and Johansen, 2014; Du et al, 2018; Saki and Bolandi, 2018) and hard-switching method (Kersting and Buss, 2017; Haj Salah et al, 2015a, 2015b; Hariprasad et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A systematic decomposition approach of nonlinear systems by combining gap metric and stability margin

Ahmadi

Haeri

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, in order to control a nonlinear dynamic system via multi-model controller, we propose a systematic approach to determine the nominal local linear models. These models are selected from the local models bank and results in a reduced nominal models set that provides enough information to design a multi-model controller. To determine the initial local models bank, gap metric is used so that the distance between two successive local models is smaller than a threshold value. Then, a systematic approach that aims to get a reduced nominal models bank is developed. Based on this approach, first, a binary gap matrix is defined by combining gap metric and stability information. Then, several rows of this matrix are selected such that the sum of them becomes a non-zero vector. The proposed approach along with a designed robust controller is validated on a pH neutralization regarding to its highly nonlinear behavior.

show abstract

A gap metric based multiple model approach for nonlinear switched systems

Cited by 41 publications

References 31 publications

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control of nonlinear process systems using empirical models

Model predictive control design for linear parameter varying systems: A survey

A systematic decomposition approach of nonlinear systems by combining gap metric and stability margin

Contact Info

Product

Resources

About