Open Access. Powered by Scholars. Published by Universities.®

Operations Research, Systems Engineering and Industrial Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Series

Computer Sciences

Neural Networks

Publication Year

Articles 1 - 4 of 4

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes Jan 2007

Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents two neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control signals are calculated using local signals only, the transient and overall system stabilities can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system and the interconnection terms, thus the requirements for exact system parameters are released. Simulation studies with a three machine power system demonstrate the effectiveness of the proposed controller designs.


Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani Oct 2006

Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Automating the task of nanomanipulation is extremely important since it is tedious for humans. This paper proposes an atomic force microscope (AFM) based force controller to push nano particles on the substrates. A block phase correlation-based algorithm is embedded into the controller for the compensation of the thermal drift which is considered as the main external uncertainty during nanomanipulation. Then, the interactive forces and dynamics between the tip and the particle, particle and the substrate are modeled and analyzed. Further, an adaptive critic NN controller based on adaptive dynamic programming algorithm is designed and the task of pushing nano particles ...


Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani Jul 2006

Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate ...


Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow Jan 2004

Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.