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Electrical and Computer Engineering

Neurocontrollers

2000

Articles 1 - 4 of 4

Full-Text Articles in Engineering

Two Separate Continually Online Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2000

Two Separate Continually Online Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of two separate continually online trained (GOT) artificial neural network (ANN) controllers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT ANN is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT ANN controllers can control turbogenerators under steady state as well as transient conditions and thus allow turbogenerators to operate more closely to their steady state stability …


Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2000

Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a neurocontroller for a turbogenerator that augments/replaces the conventional automatic voltage regulator and the turbine governor. The neurocontroller uses a novel technique based on the adaptive critic designs with emphasis on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results are presented to show that the DHP based neurocontroller is robust and performs better than the HDP based neurocontroller, as well as the conventional controller, especially when the system conditions and configuration changes.


Neurocontroller Alternatives For "Fuzzy" Ball-And-Beam Systems With Nonuniform Nonlinear Friction, Danil V. Prokhorov, Donald C. Wunsch, Paul H. Eaton Jan 2000

Neurocontroller Alternatives For "Fuzzy" Ball-And-Beam Systems With Nonuniform Nonlinear Friction, Danil V. Prokhorov, Donald C. Wunsch, Paul H. Eaton

Electrical and Computer Engineering Faculty Research & Creative Works

The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball's motion. We complicated this problem even more by not using any information concerning the ball's velocity. Although it is common to use the first differences of the ball's consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks, inputting only consecutive positions instead. We have …


Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2000

Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Turbogenerators are nonlinear time varying systems. This paper presents the design of a neurocontroller for such a turbogenerator that augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor using a novel technique based on the adaptive critic designs (ACDs) with emphasis on global dual heuristic programming (GDHP). Simulation results are presented to show that the neurocontroller derived with the GDHP approach is robust and its performance is better when compared with that derived with other neural network technique, especially when system conditions and configuration changes.