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MidSys
Identification Software for Multi-input/Multi-output
Systems on PC and Workstations

PURPOSE

Most of the digital control systems (DCS) are able to implement multi-variable algorithms for tracking and regulation, in order to take into account the eventual interactions between several loops. To adjust such algorithms, it is necessary to have a global model, which underlines interactions between the various control variables and the various process measurements.

The implementation of complex mechanical systems, with several degrees of freedom, like positioning of platforms, vehicles, flexible structures, robots, also needs a multi-input/multi-output model, showing the interactions in order to design the control laws.

It is of a great importance to identify directly a multi-input/multi-output model in order to analyse the coupling effect between the various inputs and outputs. This is possible with MidSys, but is impossible when using a concatenation of multi-input/single output models (as do other softwares which claim to be multi-variable).

MidSys brings an other advantage : the software allows to directly estimate the structure of the model to be identified from the experimental data. As a consequence, it is no more required to have an ?a priori? knowledge of the structure of the model.

This software was developped in cooperation with the Laboratoire d'Automatique de Grenoble (C.N.R.S./I.N.P.G.)



FIELDS OF APPLICATION

MidSys software can be used for any application where multi-input/multi-output control is involved : distillation columns (chemical, petro-chemical), engines (cars, aeronautics), mechanical systems with several degrees of freedom, temperature and dryness plant management, thermal systems, paper industry, cement industry, food processes, teaching, research, ...


GENERAL DESCRIPTION

This software is made of several modules, allowing the following functionalities :

* Data management

This module allows to select from a data file the different inputs and outputs of the process. It also provides the following functions :

- removing continuous components of signals (which is necessary for identifying dynamical models),
- scaling the data signals to allow a regular convergence speed for identification algorithms,
- Filtering high frequencies or drifts from these signals,

- Under-sampling of data signals (useful when too high acquisition frequency has to be used, or when a digital filter is used for anti-aliasing purpose),

* Model structure estimation

This module allows the estimation (from data) of the observability indices of multi-output systems whose sum gives the minimal state dimension of the system. These indices enables to define precisely the structure of the multi-input/multi-output model to be identified (because the number of states is not enough).

This structural estimation is made through the use of a least squares type criterion weighted by the dimension of the observability indices and using measured inputs and outputs or instrumental variables.

Recently developped structural estimation algorithms are included (the D-L. Alg.- IEEE Transactions in Automatic Control, January 1994).

* Parametric identification


This module provides several methods to identify parameters of a multi-input/multi-output model, because there is no a unique method which gives the best results for all types of measurement noise which may be encountered in practice.

Furthermore, several kinds of parametrisation of the multi-variable models are proposed, related to the control algorithm to be used later with the model.

* Validation of identified model

The aim of this module is to perform statistical tests for the validation of the model, which are more significative than visual comparisons of the plant and model outputs (because of the presence of noise). These tests are based on whiteness tests or uncorrelation tests, depending upon the identification method used.

* Analysis of the model

This module provides methods for the analysis of the model in time domain and in the frequency domain, as well as for the study of the coupling between the various transfers.

Furthermore, this module computes the poles of the multi-input/multi-output model, and converts the model into a state space form (observability or controllability canonical form).

* Simulation

This module allows to simulate the plant outputs using different signals applied to each input.


DISTRIBUTION KIT

- floppy disks containing program, help file and example data files,

- user's manual (with theory, operating instructions and examples).

2 VERSIONS

- Independant complete software under WindowsTM
(compatible with PIMTM, MatlabTM and Program CCTM)

- Toolbox for MatlabTM software (version 4.2 or higher is needed) containing functions for Model structure estimation, Parametric identification, and Validation of identified model.
Available on PC and Workstations

MINIMAL HARDWARE CONFIGURATION

PC and compatibles
Memory : minimum 1 Mo (2 Mo recommended)
1 hard disk + 1 floppy disk driver
Windows version 3.1 or more
EGA or VGA graphic screen
Mouse (highly recommended)
(Opt.) printer
(Opt.) mathematic coprocessor