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1

Fuzzy feedback linearizing controller and its equivalence with the fuzzy nonlinear internal model control structure

Boukezzoula R., Galichet S., Foulloy L.

International Journal of Applied Mathematics and Computer Science

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2007

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Vol. 17, no 2

233-248

EN

This paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model Control (NIMC) are investigated. Moreover, the paper points out that, under some conditions, both of the control structures are equivalent and naturally implement a Smith predictor in the presence of time delays.

2

Ehmac - a New Simple Tool for Robust Linear Multivariable Control

Latawiec K. J., Rojek R.

International Journal of Applied Mathematics and Computer Science

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2000

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Vol. 10, no 3

575-590

EN

A combination of long range predictive control-originated EHPC and internal model control-structured MAC is shown to produce a new, simple but effective Extended Horizon Model Algorithmic Control (EHMAC). The EHMAC strategy can be used to robustly control open-loop stable non-minimum phase (possibly non-square) MIMO systems under very large model-plant mismatches. Robust EHMAC design is made straightforward by means of a separate selection of a single prediction horizon and an IMC filter parameter, which can be easily auto-tuned.

3

Neural network based adaptive internal model control for nonlinear plants

Ma Z., Bajić V. B., Ho D. W. C.

Journal of Applied Computer Science

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1999

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Vol. 7, nr 1

63-78

EN

A novel non-parametric adaptive control method for nonlinear plants is proposed. It combines neural network (NN) based identification and internal model control (IMC) strategy. The NN is used to determine on-line an approximation of the unknown nonlinear process model. The NN parameters are updated according to the error between the plant output and the NN output. The NN can track the system output very well, so that an adaptive IMC can be implemented successfully. The design does not require computation of the inverse of the internal model of the process. Instead, it uses only system input-output data and NN output. The effectiveness of the proposed method is illustrated by a simulation experiment.

4

Neural Network Evaluation of Model-Based Residuals in Fault Detection of Time Delay Systems

Zitek P., Mankova R., Hlava J.

International Journal of Applied Mathematics and Computer Science

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1999

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Vol. 9, no 3

599-617

EN

Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.

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Design and Stability of Fuzzy Logic Multi-Regional Output Controllers

Domański P., Brdyś M. A., Tatjewski P.

International Journal of Applied Mathematics and Computer Science

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1999

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Vol. 9, no 4

883-897

EN

Design and stability analysis of fuzzy multi-regional digital controllers is considered in the paper. The controllers are based on a notion of NARMAX systems, very similar to the Takagi-Sugeno fuzzy model. The nonlinear system is approximated by a number of linear subsystems. Linear controllers are designed for all subsystems. It can be made in a classical way due to the subsystems linearity. The controllers are blended into one controller by employing fuzzy logic, the result being the fuzzy multi-regional controller (FuMR). The stability analysis of nonlinear systems with FuMR controllers composed of dynamic output feedback local linear controllers is provided. Examples illustrate the design procedure and the meaning of the stability criterion.