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

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Nov 2018

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Engineering Management and Systems Engineering Faculty Research & Creative Works

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential ...


Analyzing Sensor Based Human Activity Data Using Time Series Segmentation To Determine Sleep Duration, Yogesh Deepak Lad Jan 2018

Analyzing Sensor Based Human Activity Data Using Time Series Segmentation To Determine Sleep Duration, Yogesh Deepak Lad

Masters Theses

"Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual's activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can ...


Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun Nov 2017

Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun

Computer Science Faculty Research & Creative Works

The utilization of onsite generation system with renewable sources in manufacturing plants plays a critical role in improving the resilience, enhancing the sustainability, and bettering the cost effectiveness for manufacturers. When designing the capacity of onsite generation system, the manufacturing energy load needs to be met and the cost for building and operating such onsite system with renewable sources are two critical factors need to be carefully quantified. Due to the randomness of machine failures and the variation of local weather, it is challenging to determine the energy load and onsite generation supply at different time periods. In this paper ...


Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam Nov 2017

Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam

Engineering Management and Systems Engineering Faculty Research & Creative Works

Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another ...


Cognition-Based Approaches For High-Precision Text Mining, George John Shannon Jan 2017

Cognition-Based Approaches For High-Precision Text Mining, George John Shannon

Doctoral Dissertations

"This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in.

Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital ...


A New Reinforcement Learning Algorithm With Fixed Exploration For Semi-Markov Decision Processes, Angelo Michael Encapera Jan 2017

A New Reinforcement Learning Algorithm With Fixed Exploration For Semi-Markov Decision Processes, Angelo Michael Encapera

Masters Theses

"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition ...


A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead Jan 2017

A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead

Masters Theses

"This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve Markov and semi-Markov decision processes (or problems) in the field of airline revenue management (ARM). The ARM problem is one of control optimization in which a decision-maker must accept or reject a customer based on a requested fare. This thesis focuses on the so-called single-leg version of the ARM problem, which can be cast as a semi-Markov decision process (SMDP). Large-scale Markov decision processes (MDPs) and SMDPs suffer from the curses of dimensionality and modeling, making it difficult to create the transition probability matrices (TPMs ...


Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns Nov 2016

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their ...


Rote-Lcs Learning Classifier System For Classification And Prediction, Benjamin Daniels Jan 2015

Rote-Lcs Learning Classifier System For Classification And Prediction, Benjamin Daniels

Masters Theses

"Machine Learning (ML) involves the use of computer algorithms to solve for approximate solutions to problems with large, complex search spaces. Such problems have no known solution method, and search spaces too large to allow brute force search to be feasible. Evolutionary algorithms (EA) are a subset of machine learning algorithms which simulate fundamental concepts of evolution. EAs do not guarantee a perfect solution, but rather facilitate convergence to a solution of which the accuracy depends on a given EA's learning architecture and the dynamics of the problem.

Learning classifier systems (LCS) are algorithms comprising a subset of EAs ...


Detection And Recognition Of R/F Devices Based On Their Unintended Electromagnetic Emissions Using Stochastic And Computational Intelligence Methods, Shikhar Prasad Acharya Jan 2015

Detection And Recognition Of R/F Devices Based On Their Unintended Electromagnetic Emissions Using Stochastic And Computational Intelligence Methods, Shikhar Prasad Acharya

Doctoral Dissertations

"Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be thought of as a signature of the device. This property of uniqueness of UEEs can be used to detect and identify the device producing the emission. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band which makes them difficult to detect. There are two types of UEE detection methods. The first one is called stimulated detection method where the UEEs of a device are enhanced using ...


Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal Jan 2015

Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal

Doctoral Dissertations

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving ...


Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal Jan 2015

Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal

Doctoral Dissertations

"Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.

A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover's iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.

The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon ...


Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Oct 2009

Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility ...


Neural Network Control Of Mobile Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks Apr 2009

Neural Network Control Of Mobile Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are as and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with ...


Neural-Network-Based State Feedback Control Of A Nonlinear Discrete-Time System In Nonstrict Feedback Form, Pingan He, Jagannathan Sarangapani Dec 2008

Neural-Network-Based State Feedback Control Of A Nonlinear Discrete-Time System In Nonstrict Feedback Form, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights ...


Neural Network Output Feedback Control Of A Quadrotor Uav, Jagannathan Sarangapani, Travis Alan Dierks Dec 2008

Neural Network Output Feedback Control Of A Quadrotor Uav, Jagannathan Sarangapani, Travis Alan Dierks

Electrical and Computer Engineering Faculty Research & Creative Works

A neural network (NN) based output feedback controller for a quadrotor unmanned aerial vehicle (UAV) is proposed. The NNs are utilized in the observer and for generating virtual and actual control inputs, respectively, where the NNs learn the nonlinear dynamics of the UAV online including uncertain nonlinear terms like aerodynamic friction and blade flapping. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semi-globally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional ...


A Model Based Fault Detection And Prognostic Scheme For Uncertain Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani Dec 2008

A Model Based Fault Detection And Prognostic Scheme For Uncertain Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A new fault detection and prognostics (FDP) framework is introduced for uncertain nonlinear discrete time system by using a discrete-time nonlinear estimator which consists of an online approximator. A fault is detected by monitoring the deviation of the system output with that of the estimator output. Prior to the occurrence of the fault, this online approximator learns the system uncertainty. In the event of a fault, the online approximator learns both the system uncertainty and the fault dynamics. A stable parameter update law in discrete-time is developed to tune the parameters of the online approximator. This update law is also ...


A Model Based Fault Detection Scheme For Nonlinear Multivariable Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani Oct 2008

A Model Based Fault Detection Scheme For Nonlinear Multivariable Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel robust scheme is developed for detecting faults in nonlinear discrete time multi-input and multi-output systems in contrast with the available schemes that are developed in continuous-time. Both state and output faults are addressed by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. By using nonlinear estimation techniques, the discrete-time system is monitored online. Once a fault is detected, its dynamics are characterized using an online approximator. A stable parameter update law is developed for the online approximator scheme in discrete-time. The ...


Optimal Energy-Delay Routing Protocol With Trust Levels For Wireless Ad Hoc Networks, Eyad Taqieddin, Ann K. Miller, Jagannathan Sarangapani Sep 2008

Optimal Energy-Delay Routing Protocol With Trust Levels For Wireless Ad Hoc Networks, Eyad Taqieddin, Ann K. Miller, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the Trust Level Routing (TLR) pro- tocol, an extension of the optimized energy-delay rout- ing (OEDR) protocol, focusing on the integrity, reliability and survivability of the wireless network. TLR is similar to OEDR in that they both are link state routing proto- cols that run in a proactive mode and adopt the concept of multi-point relay (MPR) nodes. However, TLR aims at incorporating trust levels into routing by frequently changing the MPR nodes as well as authenticating the source node and contents of control packets. TLR calcu- lates the link costs based on a composite metric (delay ...


Damping Inter-Area Oscillations By Upfcs Based On Selected Global Measurements, Mahyar Zarghami, Yilu Liu, Jagannathan Sarangapani, Mariesa Crow Jul 2008

Damping Inter-Area Oscillations By Upfcs Based On Selected Global Measurements, Mahyar Zarghami, Yilu Liu, Jagannathan Sarangapani, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a method of using a selected set of the global data for controlling inter-area oscillations of the power network using unified power flow controllers. This novel algorithm utilizes reduced order observers for estimating the missing data the purpose of control when all the data is unavailable through frequency measurements in a wide area control approach. The paper will also address the problem of time-delay in data acquisition through examples.


Missouri S&T Mote-Based Demonstration Of Energy Monitoring Solution For Network Enabled Manufacturing Using Wireless Sensor Networks (Wsn), James W. Fonda, Maciej Jan Zawodniok, Al Salour, Jagannathan Sarangapani, Donald Miller Apr 2008

Missouri S&T Mote-Based Demonstration Of Energy Monitoring Solution For Network Enabled Manufacturing Using Wireless Sensor Networks (Wsn), James W. Fonda, Maciej Jan Zawodniok, Al Salour, Jagannathan Sarangapani, Donald Miller

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, an inexpensive electric utilities monitoring solution using wireless sensor networks is demonstrated that can easily be installed, deployed, maintained and eliminate unnecessary energy costs and effort. The monitoring solution is designed to support network enabled manufacturing (NEM) program using Missouri University of Science and Technology (MST), formerly the University of Missouri-Rolla (UMR), motes.


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 Mar 2008

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 ...


A Suite Of Robust Controllers For The Manipulation Of Microscale Objects, Qinmin Yang, Jagannathan Sarangapani Feb 2008

A Suite Of Robust Controllers For The Manipulation Of Microscale Objects, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A suite of novel robust controllers is introduced for the pickup operation of microscale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction, and van der Waals forces are dominant. Moreover, these forces are typically unknown. The proposed robust controller overcomes the unknown contact dynamics and ensures its performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive critic-based neural network (NN) controller, the unknown dynamic forces are estimated online. It consists of an action NN for compensating the unknown system ...


Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani Jan 2008

Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear DT systems. The proposed recursive method solves the GHJB equation in DT on a well-defined region of attraction. The definition of GHJB, pre-Hamiltonian function, HJB equation, and method of updating the control function for the affine nonlinear DT systems under small perturbation assumption are proposed ...


Predictive Congestion Control Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani Nov 2007

Predictive Congestion Control Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks, result in a large number of packet drops, unfair scenarios and low throughputs with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, this paper presents a novel, decentralized, predictive congestion control (DPCC) for wireless sensor networks (WSN). The DPCC consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel ...


Effects Of Electromagnetic Interference On Control Area Network Performance, Fei Ren, Y. Rosa Zheng, Maciej Jan Zawodniok, Jagannathan Sarangapani Nov 2007

Effects Of Electromagnetic Interference On Control Area Network Performance, Fei Ren, Y. Rosa Zheng, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the effects of electromagnetic interference (EMI) on control area network (CAN) communications are investigated by hardware experiments. Distinct CAN bit rates, communication cables, and networks are used to test effects of EMI on CAN bus. Waveforms of CAN data frames in EMI environment are observed and analyzed for figuring out details of effects. Experiments show that the EMI pulses frequently encountered in automobile and off-road machinery can cause the reduction of bit rates and errors in high-speed CAN communications. Replacing traditional unshielded parallel communication cables with shielded communication cables is proved to be an effective method of ...


Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes Oct 2007

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

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a suite of 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 inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.


Energy-Efficient Hybrid Key Management Protocol For Wireless Sensor Networks, Timothy J. Landstra, Maciej Jan Zawodniok, Jagannathan Sarangapani Oct 2007

Energy-Efficient Hybrid Key Management Protocol For Wireless Sensor Networks, Timothy J. Landstra, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we propose a subnetwork key management strategy in which the heterogeneous security requirements of a wireless sensor network are considered to provide differing levels of security with minimum communication overhead. Additionally, it allows the dynamic creation of high security subnetworks within the wireless sensor network and provides subnetworks with a mechanism for dynamically creating a secure key using a novel and dynamic group key management protocol. The proposed energy-efficient protocol utilizes a combination of pre-deployed group keys and initial trustworthiness of nodes to create a level of trust between neighbors in the network. This trust is later ...


Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes Oct 2007

Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Electrical and Computer Engineering Faculty Research & Creative Works

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of ...


Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jul 2007

Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule ...