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Full-Text Articles in Engineering
Adaptive Neural Network Identifiers For Effective Control Of Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Adaptive Neural Network Identifiers For Effective Control Of Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper provides a novel method for nonlinear identification of multiple turbogenerators in a five-machine 12-bus power system using continually online trained (COT) artificial neural networks (ANNs). Each turbogenerator in the power system is equipped with all adaptive ANN identifier, which is able to identify/model its particular turbogenerator and rest of the network to which it is connected from moment to moment, based on only local measurements. Each adaptive ANN turbogenerator can be used in the design of a nonlinear controller for each turbogenerator in a multimachine power system. Simulation results for the adaptive ANN identifiers are presented
Closed Loop Identification Of A First Order Plus Dead Time Process Model Under Pi Control, Tony Kealy, Aidan O'Dwyer
Closed Loop Identification Of A First Order Plus Dead Time Process Model Under Pi Control, Tony Kealy, Aidan O'Dwyer
Conference papers
Abstract -- This paper discusses the estimation of the parameters of a first order plus dead-time process model using the closed-loop step response data of the process under proportional plus integral (PI) control. The proportional gain and the integral time, in the PI controller, are chosen such that the closed-loop step response exhibits an under-damped response. From this response data, five characteristic points are used to determine a second order plus dead-time model and subsequently, the frequency response of the closed-loop system. Knowing the dynamics of the closed-loop system and the dynamics of the controller, the open-loop dynamics of the …
Multiple Model Networks In Non-Linear System Modelling For Control – A Review, Ruiyao Gao, Aidan O'Dwyer
Multiple Model Networks In Non-Linear System Modelling For Control – A Review, Ruiyao Gao, Aidan O'Dwyer
Conference papers
Non-linear processes, by their nature, are non-uniform and invariably require custom designed control schemes to deal with individual characteristics. No general theory deals comprehensively with the wide range of non-linear systems encountered. In an attempt to accurately model non-linear dynamical systems, a wide variety of techniques have been developed such as non-linear auto-regressive moving average with exogeneous inputs (NARMAX) models (Chen and Billings, 1989), Weiner models (Schetzen, 1981), Hammerstein models (Billings and Fakhouri, 1982) and Multiple Layer Perceptron (MLP) neural networks (Narendra and Kannan, 1990). While the accuracy of such models offers a potentially significant improvement over linear models, the …