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Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo
Proceedings of the IEEE Conference on Decision and Control | Year: 2014

Identification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. A previous contribution approaches the first using matchable-observable models originally developed in the adaptive control literature. This paper uses direct or derivative-free optimization to design filters. The accuracy, robustness and moderate computational demands of the methods is demonstrated via simulations with randomly generated models. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature. © 2014 IEEE.

Potts A.S.,University of Sao Paulo | Romano R.A.,Instituto Maua Of Tecnologia | Garcia C.,University of Sao Paulo
Control Engineering Practice | Year: 2014

Model Predictive Control (MPC) Relevant Identification (MRI) methods are a good option for identification, if there is model structure mismatch. Herein a new MRI method, named Enhanced Multistep Prediction Error Method (EMPEM), is proposed. EMPEM combines the best characteristics of others MRI methods in a single algorithm. It was developed to identify either closed-loop or open-loop systems; its convergence and stability make it perform better than the other presented methods. To show the advantages of EMPEM, a comparison is made against two other methods (one MRI and one PEM). The statistical analysis indicates that in the cases studied, the performance and the robustness of the new method is equal or better than the other ones. © 2013 Elsevier Ltd.

Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo | Garcia C.,University of Sao Paulo
IEEE International Conference on Control and Automation, ICCA | Year: 2011

The challenge of identifying multivariable models from input/output data is a subject of great interest, either in scientific works or in industrial plants. The parameterization of multi-output models is considered to be the most crucial task in a MIMO system identification procedure. In this work, a pioneering multivariable identification method is proposed, implemented and evaluated using a linear simulated plant. It is compared to other traditional MIMO identification methods and its results outperformed the other analyzed methods. It was also tested the situation of over-dimensionality of the estimated models, through the use of Hankel singular values and again the proposed method surpassed the other ones in estimating the correct model order. © 2011 IEEE.

The objective of this study was to expand the "Diagnosis of the Packing System" tool, created to evaluate and properly manage the Packing System, adding procedures that allow the evaluation of the environmental impacts, trigged in its operations. The three current indicators are systemic cost, innovation and competences. A fourth indicator was added and comprehends the treatment of solid waste, carbon dioxide emissions, water and energy consumption. Thus, the methodology that was applied in this research was exploratory and qualitative, and it was carried out through a case study, aiming to verify if the company has a systemic perspective in the phases of the development of the product, as well as to improve the tool "Diagnosis of the Packing System" in the process that is related to the chosen product. The results of the research show that it is possible to design a basic scenario, and, through it, to plan some strategies that enable the improvement of the studied system. Through its conception, the tool can be adapted to many different products of the company. The inclusion of the indicator helps the correct strategic attitude towards the world environmental policies that have been created.

Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo
Proceedings of the IEEE Conference on Decision and Control | Year: 2013

The selection of a suitable parameterization for the plant model, a crucial step in the identification of multivariable systems, has direct impact on the numerical properties of the parameter estimation algorithm.We employ a parameterization, particularly suitable for system identification, which has the following properties: observability, match-point controllability, and matchability. Using it, the number of model parameters is kept to a minimum, no undesired pole-zero cancellations can appear, and the use of nonlinear estimation is not necessary. We relate this parameterization to classical autoregressive model structures, and propose an algorithm for parameter estimation. By means of Monte Carlo simulations it is found that the algorithm is promising: fewer data points and lower signal-to-noise ratio are required to obtain results that are similar or better than those obtained by traditional methods. © 2013 IEEE.

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