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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
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 …
Experimental Implementation Of Adaptive-Critic Based Infinite Time Optimal Neurocontrol For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look
Experimental Implementation Of Adaptive-Critic Based Infinite Time Optimal Neurocontrol For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Recently the synthesis methodology for the infinite time optimal neuro-controllers for PDE systems in the framework of adaptive-critic design has been developed. In this paper, first we model an experimental setup representing one dimensional heat diffusion problems. Then we synthesize and implement an adaptive-critic based neuro-controller for online temperature profile control of the experimental setup.
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 …
Experimental Verification Of Derivatives Adaptive Critic Based Neurocontroller Performance On Single Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Experimental Verification Of Derivatives Adaptive Critic Based Neurocontroller Performance On Single Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design and real-time implementation of derivatives adaptive critic based neurocontrollers that replace the conventional automatic voltage regulators (AVRs) and turbine governors are presented in this paper. The feedback variables to the neurocontroller are completely based on local measurements from the turbogenerator. Experimental verification results are presented to show the superior performance of the derivatives adaptive critic based neurocontroller, compared to the conventional AVR and turbine governor controllers equipped with a power system stabilizer.