%0 Report %1 Ronco:1997 %A Ronco, Eric %A Gawthrop, Peter J. %D 1997 %K Control, Modelling, Modularity Networks, Neural %N CSC-97008 %T Neural Networks for Modeling and Control %X This report is a review of the main neuro-control technologies. Two main kinds of neuro-control approaches are distinguished. One entails developing a single controller from a neural network and the other one embeds a number of controllers inside a neural network. The single neuro-control approaches are mainly system inverse: the inverse of the system dynamics is used to control the system in an open loop manner. The Multi-Layer Perceptron (MLP) is widely used for this purpose although there is no guarantee that it can succeed in learning to control the plant and that, more importantly, the unclear representation it achieves prohibits the analysis of its learned control properties. These problems and the fact that open loop control is not suitable for many systems highly restricts the usefulness of the MLP for control purposes. However, the non-linear modelling capability of the MLP could be exploited to enhance model based predictive control approaches since essentially, an accurate model of the plant is all that is required to apply this method. The second neuro-control approach can be seen as a modular approach since different controllers are used for the control of different components of the systems. The main modular neuro-controllers are listed. They are all characterised by a ""gating system'' used to select the the modular units (i.e. controllers or models) valid for the computing of a current input pattern. These neural networks are referred to as the Gated Modular Neural Networks (GMNNs). Two of these networks are particularly fitted for modelling oriented control purposes. They are the Local Model Network (LMN) and the Multiple Switched Models (MSM). Since the local models of the plant are linear, it is fairly easy to transform them into controllers. For the same reason, the analysis of the properties of these networks can be easily performed and it is straightforward to determine the parameter values of the controllers as linear regression methods can be applied. These advantages among others related to a modular architecture reveal the great potential of these GMNNs for the modelling and control of non-linear systems.

@techreport{Ronco:1997, abstract = {This report is a review of the main neuro-control technologies. Two main kinds of neuro-control approaches are distinguished. One entails developing a single controller from a neural network and the other one embeds a number of controllers inside a neural network. The single neuro-control approaches are mainly system inverse: the inverse of the system dynamics is used to control the system in an open loop manner. The Multi-Layer Perceptron (MLP) is widely used for this purpose although there is no guarantee that it can succeed in learning to control the plant and that, more importantly, the unclear representation it achieves prohibits the analysis of its learned control properties. These problems and the fact that open loop control is not suitable for many systems highly restricts the usefulness of the MLP for control purposes. However, the non-linear modelling capability of the MLP could be exploited to enhance model based predictive control approaches since essentially, an accurate model of the plant is all that is required to apply this method. The second neuro-control approach can be seen as a modular approach since different controllers are used for the control of different components of the systems. The main modular neuro-controllers are listed. They are all characterised by a ""gating system'' used to select the the modular units (i.e. controllers or models) valid for the computing of a current input pattern. These neural networks are referred to as the Gated Modular Neural Networks (GMNNs). Two of these networks are particularly fitted for modelling oriented control purposes. They are the Local Model Network (LMN) and the Multiple Switched Models (MSM). Since the local models of the plant are linear, it is fairly easy to transform them into controllers. For the same reason, the analysis of the properties of these networks can be easily performed and it is straightforward to determine the parameter values of the controllers as linear regression methods can be applied. These advantages among others related to a modular architecture reveal the great potential of these GMNNs for the modelling and control of non-linear systems.}, added-at = {2009-06-26T15:25:19.000+0200}, author = {Ronco, Eric and Gawthrop, Peter J.}, biburl = {https://www.bibsonomy.org/bibtex/2dc451f8c0b53bd4114c2e1291418d24f/butz}, description = {diverse cognitive systems bib}, institution = {Faculty of Engineering, Glasgow G12 8QQ, Scotland}, interhash = {cac3207b7f0a4095cbb7dd1859478ac2}, intrahash = {dc451f8c0b53bd4114c2e1291418d24f}, keywords = {Control, Modelling, Modularity Networks, Neural}, number = {CSC-97008}, owner = {butz}, timestamp = {2009-06-26T15:25:53.000+0200}, title = {Neural Networks for Modeling and Control}, year = 1997 }