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Operations Research, Systems Engineering and Industrial Engineering Commons™
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- Neurocontrollers (7)
- Nonlinear Control Systems (7)
- Lyapunov Methods (6)
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- Discrete-time systems (4)
- Discrete Time Systems (3)
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- Formation Control (2)
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- Lyapunov Method (2)
- Observers (2)
- Power System Control (2)
- Power System Stability (2)
- Power Systems (2)
- RISE (2)
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- Wireless Sensor Network (2)
- Abstract Relation Types (ART) (1)
- Approximation theory (1)
- Asymptotic Stability (1)
- Autoregressive Moving Average Processes (1)
Articles 1 - 30 of 32
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Effects Of Electromagnetic Interference On Control Area Network Performance, Fei Ren, Y. Rosa Zheng, Maciej Jan Zawodniok, Jagannathan Sarangapani
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 …
Predictive Congestion Control Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
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 …
Workshop - Building Reflective Team Skills With A T-Group, Ray Luechtefeld, Steve Eugene Watkins
Workshop - Building Reflective Team Skills With A T-Group, Ray Luechtefeld, Steve Eugene Watkins
Engineering Management and Systems Engineering Faculty Research & Creative Works
ABET criteria require that engineering graduates have the ability to "function on multidisciplinary teams" and "communicate effectively". An important component of these skills is the ability to reflect on one's personal actions and the dynamics occurring within the group. This workshop is intended to provide participants with a practical exercise that can help students become more self-reflective and aware of group dynamics, while demonstrating the use of the "virtual facilitator" system to improve group dialogue. The workshop will engage the participants in a self- directed learning exercise modeled after T-Groups. This exercise will help participants: 1) Become aware of their …
Expert System For Team Facilitation Using Observational Learning, Ray Luechtefeld, R. K. Singh, Steve Eugene Watkins
Expert System For Team Facilitation Using Observational Learning, Ray Luechtefeld, R. K. Singh, Steve Eugene Watkins
Engineering Management and Systems Engineering Faculty Research & Creative Works
While ABET criteria require that engineering graduates be able to "function on multidisciplinary teams" and "communicate effectively", the need for effective team skills goes far deeper. One solution is the use of a computationally intelligent "virtual facilitator" that contains a subset of the expert knowledge of a skilled facilitator. The "virtual facilitator" models behaviors of an expert facilitator to engineering student teams as they are working together. Albert Bandura's theory of observational learning suggests that skills can be developed through observation of expert "others" engaged in practice. Preliminary research indicates that students can increase beneficial team behaviors (such as inquiry) …
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
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 the …
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
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
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 …
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for general multi-input and multi- output affine unknown nonlinear discrete-time systems in the presence of bounded disturbances. Adaptive critic designs consist of two entities, an action network that produces optimal solution and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function and the action network is adapted simultaneously based on the information from the critic. In our online learning method, one NN is designated as the …
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
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. …
Encouraging Lifelong Learning For Engineering Management Undergraduates, Susan L. Murray, Stephen A. Raper
Encouraging Lifelong Learning For Engineering Management Undergraduates, Susan L. Murray, Stephen A. Raper
Engineering Management and Systems Engineering Faculty Research & Creative Works
The current ABET guidelines place an emphasis on life-long learning for our undergraduate students. What is life-long learning? How can we encourage students to consider global issues, current events, or even anything "that isn't going to be on the next test"? In this paper we present survey results evaluating habits of undergraduate students entering an engineering management program and seniors related to life-long learning including attending professional society meetings, reading trade publications, reading business related books, and other learning outside of the classroom activities. This paper also presents a two semester effort to increase life-long learning activities among undergraduate engineering …
Engineering Management And Industrial Engineering: Similarities And Differences, Cassandra C. Elrod, Ashley Rasnic, William Daughton
Engineering Management And Industrial Engineering: Similarities And Differences, Cassandra C. Elrod, Ashley Rasnic, William Daughton
Business and Information Technology Faculty Research & Creative Works
Engineering Management is a broad and diverse field of engineering, thereby making it difficult to define exactly what the degree encompasses. At the same time, the somewhat related degree of Industrial Engineering is better understood. Some universities offer a Bachelor of Science degree in Engineering Management with an emphasis in Industrial Engineering, while others offer a Bachelor of Science degree in Industrial Engineering with an emphasis in Engineering Management. In today's world of competitive academia, many wonder if these degree fields are similar enough to be used interchangeably or if there is a distinct difference separating the two degrees, making …
Short-Term Stock Market Timing Prediction Under Reinforcement Learning Schemes, Hailin Li, Cihan H. Dagli, David Lee Enke
Short-Term Stock Market Timing Prediction Under Reinforcement Learning Schemes, Hailin Li, Cihan H. Dagli, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
There are fundamental difficulties when only using a supervised learning philosophy to predict financial stock short-term movements. We present a reinforcement-oriented forecasting framework in which the solution is converted from a typical error-based learning approach to a goal-directed match-based learning method. The real market timing ability in forecasting is addressed as well as traditional goodness-of-fit-based criteria. We develop two applicable hybrid prediction systems by adopting actor-only and actor-critic reinforcement learning, respectively, and compare them to both a supervised-only model and a classical random walk benchmark in forecasting three daily-based stock indices series within a 21-year learning and testing period. The …
Systems Architecting Heuristics For Systems Engineering Management And Embedded Systems Engineering, Mark S. Anderson, Cihan H. Dagli
Systems Architecting Heuristics For Systems Engineering Management And Embedded Systems Engineering, Mark S. Anderson, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Software development for United States Air Force (USAF) weapon systems is a "right here, right now" prize captured by those who can rapidly develop requirements and deliver a quality product. The Air Logistics Centers (ALCs) located at Tinker Air Force Base (AFB), Oklahoma, Warner-Robins AFB, Georgia, and Hill AFB, Utah develop requirements utilizing 3400 funding to capture this prize. The ALCs identify these requirements as corrective maintenance or perfective and adaptive maintenance. Colleen A. Calimer and John L. BeVier introduce the concept of the "Embedded Systems Engineer" in their 2004 INCOSE paper "Embedded Systems Engineering: Managing Systems Complexity, Change, and …
System Evaluation And Description Using Abstract Relation Types (Art), Joseph J. Simpson, Ann K. Miller, Cihan H. Dagli
System Evaluation And Description Using Abstract Relation Types (Art), Joseph J. Simpson, Ann K. Miller, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Two abstract relation types (ART) are developed to represent, describe and establish a computational framework for a system. An abstract relation type is closely related to and builds upon two fundamental ideas. The first idea is the binary relation and structural modeling techniques developed by John N. Warfield. The second idea is the concept of abstract data types. These two ideas are combined to create an abstract relation type that provides a structured representation and computational method for systems and system components. The complete system description approach is based on six abstract relation types: context, concept, functions, requirements, architecture, and …
Understanding Behavior Of System Of Systems Through Computational Intelligence Techniques, Nil H. Kilicay, Cihan H. Dagli
Understanding Behavior Of System Of Systems Through Computational Intelligence Techniques, Nil H. Kilicay, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
The world is facing an increasing level of systems integration leading towards systems of systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Therefore, the need to focus on overall system behavior is becoming an unavoidable issue. The problem is to develop methodologies appropriate for better understanding behavior of system of systems before the design and implementation phase. This paper focuses on computational intelligence techniques used for analysis of complex adaptive systems with the aim of …
Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow
Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow
Engineering Management and Systems Engineering Faculty Research & Creative Works
Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly …
An Evaluation Of Mahalanobis-Taguchi System And Neural Network For Multivariate Pattern Recognition, Jungeui Hong, Rajesh Jugulum, Kioumars Paryani, K. M. Ragsdell, Genichi Taguchi, Elizabeth A. Cudney
An Evaluation Of Mahalanobis-Taguchi System And Neural Network For Multivariate Pattern Recognition, Jungeui Hong, Rajesh Jugulum, Kioumars Paryani, K. M. Ragsdell, Genichi Taguchi, Elizabeth A. Cudney
Engineering Management and Systems Engineering Faculty Research & Creative Works
The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.
Effective Use Of Process Capability Indices For Supplier Management, Elizabeth A. Cudney, David Drain
Effective Use Of Process Capability Indices For Supplier Management, Elizabeth A. Cudney, David Drain
Engineering Management and Systems Engineering Faculty Research & Creative Works
Process capability indices were originally invented to enable an organization to make economically sound decisions for process management. Process capability is a comparison of the voice of the process with the voice of the customer. Current practice is to use Cp and Cpk regardless of the validity of the underlying assumptions necessary for their use. Even if all necessary assumptions are satisfied, important problems can be missed if these indices are the sole process evaluation examined. Customer-supplier axioms are introduced to motivate more useful process evaluations and foster long-term harmonious relationships. This paper explores the alternative capability indices Cpm, Cpmk, …
Breaking The Cycle-Preventing Failures By Leveraging Historical Data During Conceptual Design, Daniel A. Krus, Katie Grantham
Breaking The Cycle-Preventing Failures By Leveraging Historical Data During Conceptual Design, Daniel A. Krus, Katie Grantham
Engineering Management and Systems Engineering Faculty Research & Creative Works
Major engineering accidents are often caused by seemingly minor failures propagating through complex systems. One example of this is an accident involving a Bell 206 Rotorcraft where a fuel pump failure led to the severing of the tail boom. Cataloguing and communicating the knowledge of potential failures and failure propagations is critical to prevent further accidents. The need for effective failure prevention tools is not specific to rotorcrafts, however. Failure reporting systems have been adopted by various industries to aid and promote failure prevention. The catalogued failures usually consist of narratives describing which part of a product failed, how it …
Asymptotic Stability Of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks, Jagannathan Sarangapani, Travis Alan Dierks
Asymptotic Stability Of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks, Jagannathan Sarangapani, Travis Alan Dierks
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) 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 asymptotically stable and the NN weights are bounded as …
An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani
An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results.
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, 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, 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. …
Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel online reinforcement learning neural network (NN)-based optimal output feedback controller, referred to as adaptive critic controller, is proposed for affine nonlinear discrete-time systems, to deliver a desired tracking performance. The adaptive critic design consist of three entities, an observer to estimate the system states, an action network that produces optimal control input and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function which is based on the standard Bellman equation. By using the Lyapunov approach, the uniformly ultimate boundedness …
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate …
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
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.
Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani
Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Literature indicates that spatial diversity can be utilized to compensate channel uncertainties such as multipath fading. Therefore, in this paper, spatial diversity is exploited for locating stationary and mobile objects in the indoor environment. First, space diversity technique is introduced for small scale motion and temporal variation compensation of received signal strength and it is demonstrated analytically that it enhances location accuracy. Small scale motion refers to movements of the transmitter and/or the receiver of the order of sub-wavelengths while temporal effects refer to environmental variations with time. A novel metric is introduced for selection combining in order to improve …
Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, …
Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke
Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke
Electrical and Computer Engineering Faculty Research & Creative Works
The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a …
Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) 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 asymptotically stable and the NN weights are bounded as opposed …
Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel neural network (NN) based online reinforcement learning controller is designed for nonaffine nonlinear discrete-time systems with bounded disturbances. The nonaffine systems are represented by nonlinear auto regressive moving average with exogenous input (NARMAX) model with unknown nonlinear functions. An equivalent affine-like representation for the tracking error dynamics is developed first from the original nonaffine system. Subsequently, a reinforcement learning-based neural network (NN) controller is proposed for the affine-like nonlinear error dynamic system. The control scheme consists of two NNs. One NN is designated as the critic, which approximates a predefined long-term cost function, whereas an …