Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms
Intelligent Control of Discrete Linear Systems Based on a Supervised Adaptive Multiestimation Scheme
Journal of Intelligent and Robotic Systems
Stability analysis of discrete-time fuzzy dynamic systems based on piecewise Lyapunov functions
IEEE Transactions on Fuzzy Systems
Adaptive switching control of LTI MIMO systems using a family of controllers approach
Automatica (Journal of IFAC)
Brief Inference of candidate loop performance and data filtering for switching supervisory control
Automatica (Journal of IFAC)
Stable multi-estimation model for single-input single-output discrete adaptive control systems
International Journal of Systems Science
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This paper develops a representation of multi-model based controllers by using artificial intelligence typical structures. These structures will be neural networks, genetic algorithms and fuzzy logic. The interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented. The proposed methodology allows to extend the existing single model based neural controllers to multimodel based ones, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms and fuzzy logic to multimodel controller design are proposed. Thus, the mutation operation from genetic algorithms inspires a robustness test which consists of a random modification of the estimates which is used to select the estimates leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classical multimodel schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multiestimation structures which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design.