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Full-Text Articles in Engineering
An Adaptive Neural Network Identifier For Effective Control Of A Static Compensator Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Mariesa Crow, Ronald G. Harley
An Adaptive Neural Network Identifier For Effective Control Of A Static Compensator Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Mariesa Crow, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.
A Ram-Based Neural Network For Collision Avoidance In A Mobile Robot, Qiang Yao, Daryl G. Beetner, Donald C. Wunsch, Bjorn Osterloh
A Ram-Based Neural Network For Collision Avoidance In A Mobile Robot, Qiang Yao, Daryl G. Beetner, Donald C. Wunsch, Bjorn Osterloh
Electrical and Computer Engineering Faculty Research & Creative Works
A RAM-based neural network is being developed for a mobile robot controlled by a simple microprocessor system. Conventional neural networks often require a powerful and sophisticated computer system. Training a multi-layer neural network requires repeated presentation of training data, which often results in very long learning time. The goal for this paper is to demonstrate that RAM-based neural networks are a suitable choice for embedded applications with few computational resources. This functionality is demonstrated in a simple robot powered by an 8051 microcontroller with 512 bytes of RAM. The RAM-based neural network allows the robot to detect and avoid obstacles …
Neuroidentification Of System Parameters For The Shunt & Series Branch Control Of Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani, Mariesa Crow
Neuroidentification Of System Parameters For The Shunt & Series Branch Control Of Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) forms an affective means for controlling the power flow. The UPFC consists of shunt and series inverters which are conventionally controlled using linear controllers. This paper presents the design of neuroidentifiers that identify the system parameters that determine the UPFC controller outputs one-time step ahead thus, making the pathway for the design of adaptive neurocontrollers. Two neuroidentifiers are used for identifying the nonlinear dynamics of power system and UPFC, one neuroidentifier for the shunt inverter and the other for the series …
Adaptive Critic Designs And Their Implementations On Different Neural Network Architectures, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Designs And Their Implementations On Different Neural Network Architectures, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures.
Neuro Emission Controller For Minimizing Cyclic Dispersion In Spark Ignition Engines, Pingan He, Jagannathan Sarangapani
Neuro Emission Controller For Minimizing Cyclic Dispersion In Spark Ignition Engines, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel neural network (NN) controller is developed to control spark ignition (SI) engines at extreme lean conditions. The purpose of neurocontroller is to reduce the cyclic dispersion at lean operation even when the engine dynamics are unknown. The stability analysis of the closed-loop control system is given and the boundedness of all signals is ensured. Results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller. The neuro controller can also be extended to minimize engine emissions with high EGR levels, where similar complex cyclic dynamics are observed. Further, the proposed approach can be applied to control …
Proper Orthogonal Decomposition Based Modeling And Experimental Implementation Of A Neurocontroller For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look, Radhakant Padhi
Proper Orthogonal Decomposition Based Modeling And Experimental Implementation Of A Neurocontroller For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look, Radhakant Padhi
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Experimental implementation of a dual neural network based optimal controller for a heat diffusion system is presented. Using the technique of proper orthogonal decomposition (POD), a set of problem-oriented basis functions are designed taking the experimental data as snap shot solutions. Using these basis functions in Galerkin projection, a reduced-order analogous lumped parameter model of the distributed parameter system is developed. This model is then used in an analogous lumped parameter problem. A dual neural network structure called adaptive critics is used to obtain optimal neurocontrollers for this system. In this structure, one set of neural networks captures the relationship …
Adaptive-Critic-Based Optimal Neurocontrol For Synchronous Generators In A Power System Using Mlp/Rbf Neural Networks, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive-Critic-Based Optimal Neurocontrol For Synchronous Generators In A 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 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 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 in turn compared …
A Novel Dual Heuristic Programming Based Optimal Control Of A Series Compensator In The Electric Power Transmission System, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Novel Dual Heuristic Programming Based Optimal Control Of A Series Compensator In The Electric Power Transmission System, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controller (CONVC) in the internal control of a power electronic converter based series compensator in the electric power transmission system. The performance of the proposed DHP based neurocontroller is compared with that of the CONVC with respect to damping low frequency oscillations. Simulation results using the PSCAD/EMTDC software package are presented.
Approximate Dynamic Programming Based Optimal Neurocontrol Synthesis Of A Chemical Reactor Process Using Proper Orthogonal Decomposition, Radhakant Padhi, S. N. Balakrishnan
Approximate Dynamic Programming Based Optimal Neurocontrol Synthesis Of A Chemical Reactor Process Using Proper Orthogonal Decomposition, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The concept of approximate dynamic programming and adaptive critic neural network based optimal controller is extended in this study to include systems governed by partial differential equations. An optimal controller is synthesized for a dispersion type tubular chemical reactor, which is governed by two coupled nonlinear partial differential equations. It consists of three steps: First, empirical basis functions are designed using the "Proper Orthogonal Decomposition" technique and a low-order lumped parameter system to represent the infinite-dimensional system is obtained by carrying out a Galerkin projection. Second, approximate dynamic programming technique is applied in a discrete time framework, followed by the …
Adaptive Critic Design Based Neurocontroller For A Statcom Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Design Based Neurocontroller For A Statcom Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A novel nonlinear optimal neurocontroller for a static compensator (STATCOM) connected to a power system using artificial neural networks is presented in this paper. The heuristic dynamic programming (HDP), a member of the adaptive critic designs (ACDs) family, is used for the design of the STATCOM neurocontroller. This neurocontroller provides nonlinear optimal control with better performance compared to the conventional PI controllers.
Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller's training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in …
A Continually Online Trained Neurocontroller For The Series Branch Control Of The Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani
A Continually Online Trained Neurocontroller For The Series Branch Control Of The Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of a neurocontroller that controls the power flow and regulates voltage along a transmission line. The continually online neurocontroller is used for controlling the series inverter of UPFC. Simulation results carried out in the PSCAD/EMTDC environment are presented …
A Heuristic Dynamic Programming Based Power System Stabilizer For A Turbogenerator In A Single Machine Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
A Heuristic Dynamic Programming Based Power System Stabilizer For A Turbogenerator In A Single Machine Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power …
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of nonlinear optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP-based neurocontrol is much more effective than the conventional proportional-integral-derivative control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …