Andere Kunden interessierten sich auch für
![Nonlinear Adaptive Robust Control of Electro-hydraulic Systems]( "Nonlinear Adaptive Robust Control of Electro-hydraulic Systems")
Nonlinear Adaptive Robust Control of Electro-hydraulic Systems51,99 €![Robust and Adaptive Model Predictive Control of Nonlinear Systems]( "Robust and Adaptive Model Predictive Control of Nonlinear Systems")
Robust and Adaptive Model Predictive Control of Nonlinear Systems153,99 €![Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence]( "Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence")
Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence74,99 €![Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence]( "Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence")
Model-Based Control of Flying Robots for Robust Interaction Under Wind Influence74,99 €![Methods of Model Based Process Control]( "Methods of Model Based Process Control")
Methods of Model Based Process Control383,99 €![Mechanics and Model-Based Control of Smart Materials and Structures]( "Mechanics and Model-Based Control of Smart Materials and Structures")
Mechanics and Model-Based Control of Smart Materials and Structures116,99 €![Model Based Predictive Networked Control Systems]( "Model Based Predictive Networked Control Systems")
Model Based Predictive Networked Control Systems32,99 €
Adaptive tracking of nonlinear dynamic plants is currently an important area of research. The main difficulty being felt by the research community is the lack of a general modelling framework that can facilitate synthesis of a simplistic control law, while being capable of providing accurate approximation of nonlinear systems. The aim of this study is to alleviate that problem by introducing a novel technique based on the control-oriented U- Model for the adaptive tracking of a wide range of stable nonlinear dynamic plants using only input- output data. The overall scheme is based on the robust internal model control (IMC) structure wherein different internal models, using nonlinear adaptive filtering and higher-order neural networks, are used. In each case, the U-Model equivalence of the internal model is developed and a simplistic control law based on polynomial root-solving is synthesized. The effectiveness of the proposed adaptive schemes is demonstrated through simulations and real-time applications to a variety of nonlinear plants.