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Full-Text Articles in Engineering
Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under …
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; …
Control Technique For Series Input-Parallel Output Converter Topologies, Jonathan W. Kimball, Joseph T. Mossoba, Philip T. Krein
Control Technique For Series Input-Parallel Output Converter Topologies, Jonathan W. Kimball, Joseph T. Mossoba, Philip T. Krein
Electrical and Computer Engineering Faculty Research & Creative Works
A series input-parallel output DC-DC converter topology inherently provides output current sharing among the phases, provided the input voltages are forced to share. With conventional output voltage feedback controls, input voltage sharing is unstable. Recent literature work proposes complicated feedback loops to provide stable voltage sharing, at the expense of dynamic performance. In the current work, a simple controller based on the sensorless current mode approach (SCM) stabilizes voltage sharing without compromising system performance. The SCM controllers reject source disturbances, and allow the output voltage to be tightly regulated by additional feedback control. With SCM control in place, a #super-matched# …
Series-Parallel Approaches And Clamp Methods For Extreme Dynamic Response With Advanced Digital Loads, Philip T. Krein, Jonathan W. Kimball
Series-Parallel Approaches And Clamp Methods For Extreme Dynamic Response With Advanced Digital Loads, Philip T. Krein, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
The series-input parallel-output dc-dc converter combination provides inherent sharing among the converters. With conventional controls, however, this sharing is unstable. Recent literature work proposes complicated feedback loops to correct the problem, at the cost of dynamic performance. This paper shows that a simple sensorless current mode control stabilizes sharing with fast dynamics suitable for advanced digital loads. With this control in place, a "super-matched" current sharing control emerges. Sharing occurs through transients, limited only by the energy limits of the converters. The control approach has considerable promise for high-performance voltage regulator modules. For even faster response, clamping techniques are proposed.
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents a novel load frequency control design approach for a two-area power system that relies on unsupervised and supervised learning neural network structure. Central to this approach is the prediction of the load disturbance of each area at every minute interval that is uniquely assigned to a cluster via unsupervised learning process. The controller feedback gains corresponding to each cluster center are determined using modal control technique. Thereafter, supervised learning neural network (SLNN) is employed to learn the mapping between each cluster center and its feedback gains. A real time load disturbance in either or both areas activates …
Rate-Based End-To-End Congestion Control Of Multimedia Traffic In Packet Switched Networks, Mingsheng Peng, S. R. Subramanya, Jagannathan Sarangapani
Rate-Based End-To-End Congestion Control Of Multimedia Traffic In Packet Switched Networks, Mingsheng Peng, S. R. Subramanya, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes an explicit rate-based end-to-end congestion control mechanism to alleviate congestion of multimedia traffic in packet switched networks such as the Internet. The congestion is controlled by adjusting the transmission rates of the sources in response to the feedback information from destination such as the buffer occupancy, packet arrival rate and service rate at the outgoing link, so that a desired quality of service (QoS) can be met. The QoS is defined in terms of packet loss ratio, transmission delay, power, and network utilization. Comparison studies demonstrate the effectiveness of the proposed scheme over New-Reno TCP (a variant …
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.
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 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 PID 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 …
Robust Control Of Input Limited Smart Structural Systems, Sridhar Sana, Vittal S. Rao
Robust Control Of Input Limited Smart Structural Systems, Sridhar Sana, Vittal S. Rao
Electrical and Computer Engineering Faculty Research & Creative Works
Integration of controllers with smart structural systems require the controllers to consume less power and to be small in hardware size. These requirements pose as limits on the control input and the order of the controllers. Use of reduced order model of the plant in the controller design can cause spill over problems in the closed-loop system due to possible excitation of the unmodeled dynamics. In this paper, we present a method to design output feedback robust controllers for smart structures in the presence of control input limits considering unmodeled dynamics as additive uncertainty in the design. The performance requirements …
Statcom Control For Power System Voltage Control Applications, Pranesh Rao, Zhiping Yang, Mariesa Crow
Statcom Control For Power System Voltage Control Applications, Pranesh Rao, Zhiping Yang, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
A static compensator (STATCOM) is a device that can provide reactive support to a bus. It consists of voltage sourced converters connected to an energy storage device on one side and to the power system on the other. In this paper the conventional method of PI control is compared and contrasted with various feedback control strategies. A linear optimal control based on LQR control is shown to be superior in terms of response profile and control effort required. These methodologies are applied to an example power system
Security Assessment Using Neural Computing, Badrul H. Chowdhury, B. M. Wilamowski
Security Assessment Using Neural Computing, Badrul H. Chowdhury, B. M. Wilamowski
Electrical and Computer Engineering Faculty Research & Creative Works
The advantage of fast computation capability of an artificial neural network (ANN) is used to introduce an iterative scheme for security assessment of power systems. Two related approaches are shown which demonstratedly work satisfactorily. The idea of feedback in a single-layer feedforward neural network is experimented yielding higher accuracy. The ANN is trained by using a set of data obtained from off-line analysis of the power network. After training, an approximate solution for a given condition may be found almost immediately. The approximate solution obtained is judged adequate for assessing the security of the power system. A case study is …