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Full-Text Articles in Engineering
Query-Based Learning For Aerospace Applications, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Query-Based Learning For Aerospace Applications, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Electrical and Computer Engineering Faculty Research & Creative Works
Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper …
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.
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming …
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 …
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 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.