Rodrigo Alvite Romano

Enhancement in performance and stability of MRI methods

Abstract Two representative approaches for MRI (MPC Relevant Identification) methods are reported in the literature. The first one is based on the solution of an optimal problem, while the second is based on the prefiltering of the system input and output signals. Each method has advantages and disadvantages in accordance with the process to identify, the length of the prediction horizon or its mathematical implementation. A new MRI method is proposed herein, based on the advantages of both algorithms. A comparison is performed among some MRI methods and the new proposed one. The results indicate that in the studied case, the performance of the new method is better.

Matchable-Observable Linear Models and Direct Filter Tuning: An Approach to Multivariable Identification

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. Approaching the first using matchable-observable models originally developed in the adaptive control literature and the second via direct or derivative-free optimization, effective least-squares algorithms can be used for parameter estimation. The accuracy, robustness and moderate computational demands of the methods proposed are demonstrated via simulations with randomly generated models and applied to identification using real process data. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature.

Multivariable system identification using an output-injection based parameterization

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.

Matchable-observable linear models for multivariable identification: Structure selection and experimental results

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. In previous contributions we approached the first using matchable-observable models originally developed in the adaptive control literature, and used direct or derivative-free optimization to design filters. In this paper we show a simple and effective structure-selection method and demonstrate its accuracy, robustness and moderate computational demands using data from an industrial evaporator and experimental results with a twin rotor.

Direct filter tuning and optimization in multivariable identification

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.

Linear multivariable identification using observable state space parameterizations

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.

Improving performance and stability of MRI methods in closed-loop

Two representative approaches for MRI methods are reported in the literature. The first one is based on the solution of an optimal problem, while the second is based on the prefiltering of the system input and output signals. Each method has advantages and disadvantages according to the process to identify, the length of the prediction horizon or its mathematical implementation. Herein a new MRI method is proposed (C-EMPEM), based on the advantages of both algorithms and on some improvements. The new method was developed to identify either closed-loop or open-loop systems. A comparison is performed among some MRI and PEM methods and the new one proposed, considering a closed-loop system. The results indicate that in the studied case, the performance of the new method is better.

Obtaining multivariable continuous-time models from sampled data

While most physical systems or phenomena occur in continuous-time, identification methods based on discrete-time models are more widespread among practitioners and academic community, possibly due to the discrete-time nature of the data records. There has been a growing interest in estimating continuous-time (CT) models in the last decade. This work develops algorithms to estimate the parameters of multivariable state-space CT models from input-output samples using a method based on the recently developed MOLI-ZOFT approach. The performance of the algorithm is evaluated using real data from an industrial winding process.

Recursive identification of multivariable systems using matchable-observable linear models

This paper presents a recursive parameter estimation algorithm based on a matchable-observable parameterization of multivariable process models. As a consequence of the properties of the models used, no undesired pole-zero cancellations appear, the number of model parameters is not excessive, linear least-squares estimation methods are applicable, and parameter estimation can be accomplished without the need for iterative or nonlinear optimization. The performance of the algorithm developed is assessed in comparison with a well-established recursive subspace method, in a simulation study with time-invariant and time-varying scenarios. The results obtained demonstrate the accuracy and effectiveness of the proposed approach.

State space LPV model identification using LS-SVM: A case-study with dynamic dependence

In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.