Plantwide control design using latent variables: An integration between control allocation and a measurement combination approach. (December 2022)

• Record Type: Journal Article
• Authors: Luppi, P.A.; Rodríguez del Portal, S.; Braccia, L.; Zumoffen, D.
• Is Part Of: Journal of process control. Volume 120(2022)
• Page Start: 159
• Page End: 176
• Publication Date: 2022-12
• Subjects: Plantwide control -- Latent variables -- Principal component analysis -- Control allocation -- Measurement combination -- Multiobjective optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.11.007
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This paper presents a plantwide control design methodology based on a novel structure which consists of a decentralized strategy complemented by a control allocation (CA) module and a measurement combination (MC) block. Taking into account a principal components analysis (PCA) selection approach, the CA and MC modules perform a dimensional reduction of the original input–output variable space in order to obtain sets of latent variables (or principal components) as control actions and controlled variables. The use of principal components in the controller design provides several interesting features given that: (i) the conditioning of the subsystem to be controlled can be improved, (ii) when performing combinations of variables, the CA and MC modules act as steady-state decouplers and thus an apparently diagonal process is obtained, which favors the reduction of the variables interaction and the pairing problem is automatically solved, and (iii) they allow to naturally handle nonsquare systems. The proposed design procedure is implemented through a multiobjective bilevel mixed-integer nonlinear programming (BMINLP) optimization problem. The leader problem is based on the minimization of three functional costs: 1- the well-known sum of squared deviations (SSD) index, 2- the number of selected manipulated variables (actuators), and 3- the number of selected measurements (sensors). The inner optimization minimizes the relative gain array number (RGAN). This provides a good trade-off between the degree of conditioning/controllability and the complexity/cost of the resulting system. This problem is efficiently solved through genetic algorithms and allows to perform: (i) the selection of the manipulated variables (actuators) and the measurements (sensors) to be used, (ii) the computation of the matrices that characterize the CA and MC modules, and (iii) the stability analysis of the multivariable control structure. The overall design procedure only requires steady-state models of the process. The Tennessee Eastman case study is considered for the simulation and performance evaluation of the proposed solutions. Highlights: Control allocation and measurement combination integrated to decentralized control. Control actions and controlled variables are defined as principal components. Better system conditioning, steady-state decoupling, and nonsquare systems handling. Trade-off between controllability and complexity/cost via multiobjective optimization. Procedure for input–output selection, control configuration, and stability analysis.