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Full-Text Articles in Engineering
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For Indirect Adaptive Control Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For Indirect Adaptive Control Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper compares the performances of a multilayer perceptron neurocontroller and a radial basis function neurocontroller for backpropagation through time based indirect adaptive control of the synchronous generator. Also, the neurocontrollers are compared with the conventional controller for small as well as large disturbances to the power system
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.
Adaptive Critic Based Optimal Neurocontrol For Synchronous Generator In Power System Using Mlp/Rbf Neural Networks, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Based Optimal Neurocontrol For Synchronous Generator In Power System Using Mlp/Rbf Neural Networks, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC),which consists of the automatic voltage regulator (AVR) and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design (ACD) technique is used for the design of the neurocontroller. The performance of the MLPN based HDP neurocontroller (MHDPC) is compared with the RBFN based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are …
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a …