Multiple Model Approaches To Nonlinear Modelling And Control
R Murray-Smith, T. Johansen
CRC Press, Mar 19, 1997 - Technology & Engineering - 360 pages
This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.
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adaptive control analysis applied approach approximation chapter complex condition number control laws controller design data set decomposition defined described dynamic systems equation error estimated example fuzzy clustering fuzzy control system fuzzy logic fuzzy sets gain scheduling Gaussian given global learning heterogeneous controller identification IEEE IEEE Trans input space input-output Johansen Kalman filter Kuipers Laguerre learner learning algorithms Linear Matrix Inequalities linear models LTS fuzzy model Markov maximisation membership functions methods minimise Mixture of Experts MMAC model networks model or controller model parameters model structure modelling and control multiple model Murray-Smith muscle neural network noise nonlinear model nonlinear systems normalisation operating regime based optimal optimisation overlap partition partition of unity performance plant prediction problem Proc qualitative reactor regions regression representation robust shown in Figure simulation stability state-feedback state-feedback controller state-space Sugeno techniques training set transition uncertainties validity functions values variables variance vector weighting functions
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