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Missouri University of Science and Technology
Electrical and Computer Engineering Faculty Research & Creative Works
- Keyword
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- Nonlinear Control Systems (5)
- Adaptive Control (4)
- Learning (Artificial Intelligence) (4)
- Neurocontrollers (4)
- Observers (4)
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- Closed Loop Systems (3)
- Discrete Time Systems (3)
- Lyapunov Methods (3)
- Backpropagation (2)
- Feedback (2)
- Feedforward Neural Nets (2)
- Intelligent Structures (2)
- Internal Combustion Engines (2)
- Neural Networks (NNs) (2)
- Optimal Control (2)
- Adaptive Critic (1)
- Adaptive Critic Design (1)
- Adaptive Critic-Based Neural Network Controller (1)
- Aerodynamic Condition (1)
- Aerodynamics (1)
- Aerospace Computing (1)
- Aerospace Engineering (1)
- Aerospace Materials (1)
- Air Speeds (1)
- Aluminum oxide layer (1)
- Angle of Attack (1)
- Backpropagation Training Algorithm (1)
- Bars (Metal) (1)
- Battery Modeling (1)
- Battery Soc (1)
Articles 1 - 25 of 25
Full-Text Articles in Engineering
Oxidation Layer Formation On Aluminum Substrates With Surface Defects Using Molecular Dynamics Simulation, Emmanuel Olugbade, Hiep Pham, Yuchu He, Haicheng Zhou, Chulsoon Hwang, Jonghyun Park
Oxidation Layer Formation On Aluminum Substrates With Surface Defects Using Molecular Dynamics Simulation, Emmanuel Olugbade, Hiep Pham, Yuchu He, Haicheng Zhou, Chulsoon Hwang, Jonghyun Park
Electrical and Computer Engineering Faculty Research & Creative Works
Aluminum Oxide Layer Affects the Integrity of Electrical Contact and Can Contribute Adversely to Passive Intermodulation (PIM) Behavior in Radio Frequency (RF) Devices, necessitating a Need for Understanding its Formation Mechanism and Realistic Estimation of its Thickness. using ReaxFF Molecular Dynamics Simulation Technique, This Study Investigated the Impact of Surface Defects on Aluminum Oxide Layer Formation. Results Reveal that Crystallographic Orientation Did Not Affect the Kinetics of Oxidation Process of Aluminum. However, the Reaction Kinetics Increased Significantly with Surface Inhomogeneities Such as Cracks, Scratches, and Grain Boundaries. a Non-Uniform Oxide Layer with Thickness Variation in the Range of 72-77% Was …
Laser-Scribed Conductive, Photoactive Transition Metal Oxide On Soft Elastomers For Janus On-Skin Electronics And Soft Actuators, Ganggang Zhao, Yun Ling, Yajuan Su, Zanyu Chen, Cherian J. Mathai, Ogheneobarome Emeje, Alexander Brown, Dinesh Reddy Alla, Jie Huang, Chansong Kim, Qian Chen, Xiaoqing He, David Stalla, Yadong Xu
Laser-Scribed Conductive, Photoactive Transition Metal Oxide On Soft Elastomers For Janus On-Skin Electronics And Soft Actuators, Ganggang Zhao, Yun Ling, Yajuan Su, Zanyu Chen, Cherian J. Mathai, Ogheneobarome Emeje, Alexander Brown, Dinesh Reddy Alla, Jie Huang, Chansong Kim, Qian Chen, Xiaoqing He, David Stalla, Yadong Xu
Electrical and Computer Engineering Faculty Research & Creative Works
Laser-assisted fabrication of conductive materials on flexible substrates has attracted intense interests because of its simplicity, easy customization, and broad applications. However, it remains challenging to achieve laser scribing of conductive materials on tissue-like soft elastomers, which can serve as the basis to construct bioelectronics and soft actuators. Here, we report laser scribing of metallic conductive, photoactive transition metal oxide (molybdenum dioxide) on soft elastomers, coated with molybdenum chloride precursors, under ambient conditions. Laser-scribed molybdenum dioxide (LSM) exhibits high electrical conductivity, biocompatibility, chemical stability, and compatibility with magnetic resonance imaging. In addition, LSM can be made on various substrates (polyimide, …
An Accurate And Computationally Efficient Method For Battery Capacity Fade Modeling, D. M. Ajiboye, Jonathan W. Kimball, R.(Robert) G. Landers, John (T.) Park
An Accurate And Computationally Efficient Method For Battery Capacity Fade Modeling, D. M. Ajiboye, Jonathan W. Kimball, R.(Robert) G. Landers, John (T.) Park
Electrical and Computer Engineering Faculty Research & Creative Works
The Industry Demand for Accurate and Fast Algorithms that Model Vital Battery Parameters, E.g., State-Of-Health, State-Of-Charge, Pulse-Power Capability, is Substantial. One of the Most Critical Models is Battery Capacity Fade. the Key Challenge with Physics-Based Battery Capacity Fade Modeling is the High Numerical Cost in Solving Complex Models. in This Study, an Efficient and Fast Model is Presented to Capture Capacity Fade in Lithium-Ion Batteries. Here, the High-Order Chebyshev Spectral Method is Employed to Address the Associated Complexity with Physics-Based Capacity Fade Models. its Many Advantages, Such as Low Computational Memory, High Accuracy, Exponential Convergence, and Ease of Implementation, Allow …
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Electrical and Computer Engineering Faculty Research & Creative Works
Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set, …
Integration Of Microwave And Thermographic Ndt Methods For Corrosion Detection, Dustin F. Pieper, Kristen M. Donnell, Mohammad Tayeb Ahmad Ghasr, Edward C. Kinzel
Integration Of Microwave And Thermographic Ndt Methods For Corrosion Detection, Dustin F. Pieper, Kristen M. Donnell, Mohammad Tayeb Ahmad Ghasr, Edward C. Kinzel
Electrical and Computer Engineering Faculty Research & Creative Works
Infrastructure health monitoring is an important issue in the transportation industry. For the case of cement-based structures in particular, detection of corrosion on reinforcing steel bars (rebar) is an ongoing problem for aging infrastructure. There have been a number of techniques that have shown promise in this area including microwave nondestructive testing (NDT) and thermography. Thermography is quite advantageous as it is an established method, and can be utilized for large inspection areas with intuitive results. Typical heat sources include induction heating and flash lamps, but these are not without drawbacks. Microwave nondestructive testing has also been successful at detecting …
Organizing A Student Poster Session In An Asee Section Conference, Steve Eugene Watkins, Les Kinsler, Julia L. Morse, Douglas R. Carroll
Organizing A Student Poster Session In An Asee Section Conference, Steve Eugene Watkins, Les Kinsler, Julia L. Morse, Douglas R. Carroll
Electrical and Computer Engineering Faculty Research & Creative Works
Student poster sessions at conferences can be valuable experiences for undergraduate and graduate students and can enrich the conference program for all participants. Student poster presentations beyond the local campus can provide additional experience in professional communication (especially in preparing succinct abstracts and in effective visual design), can prepare students for future conference participations, and can facilitate student-faculty interaction. Several issues exist when including student poster sessions in engineering education conferences. How can the content of posters be related to an engineering education theme? How are communication principles of audience and purpose incorporated into the session guidelines and review process? …
Computational Modeling And Experimental Study On Optical Microresonators Using Optimal Spherical Structure For Chemical Sensing, Hanzheng Wang, Lei Yuan, Jie Huang, Xinwei Lan, Cheol-Woon Kim, Lan Jiang, Hai Xiao
Computational Modeling And Experimental Study On Optical Microresonators Using Optimal Spherical Structure For Chemical Sensing, Hanzheng Wang, Lei Yuan, Jie Huang, Xinwei Lan, Cheol-Woon Kim, Lan Jiang, Hai Xiao
Electrical and Computer Engineering Faculty Research & Creative Works
Chemical sensors based on optical microresonators have been demonstrated highly sensitive by monitoring the refractive index (RI) changes in the surrounding area near the resonator surface. In an optical resonator, the Whispering Gallery Modes (WGMs) with high quality (Q) factor supported by the spherical symmetric structure interacts with the contiguous background through evanescent field. Highly sensitive detection can be realized because of the long lifetime of the photons. The computational models of solid glass microspheres and hollow glass spheres with porous wall (PW-HGM) were established. These two types of microresonators were studied through simulations. The PWHGM resonator was proved as …
Nanostructured Substrate With Nanoparticles Fabricated By Femtosecond Laser For Surface-Enhanced Raman Scattering, Yukun Han, Zhi Liang, Huilai Sun, Hai Xiao, Hai-Lung Tsai
Nanostructured Substrate With Nanoparticles Fabricated By Femtosecond Laser For Surface-Enhanced Raman Scattering, Yukun Han, Zhi Liang, Huilai Sun, Hai Xiao, Hai-Lung Tsai
Electrical and Computer Engineering Faculty Research & Creative Works
A Simple and Fast Method to Fabricate Nanostructured Substrates with Silver Nanoparticles over a Large Area for Surface-Enhanced Raman Scattering (SERS) is Reported. the Method Involves Two Steps: (1) Dip the Substrate into a Silver Nitrate Solution for a Few Minutes, Remove the Substrate from the Solution, and Then Air Dry and (2) Process the Silver Nitrate Coated Substrate by Femtosecond (Fs) Laser Pulses in Air. the Second Step Can Create Silver Nanoparticles Distributed on the Nanostructured Surface of the Substrate by the Photoreduction of Fs Multiphoton Effects. This Study Demonstrates that an Enhancement Factor (EF) Greater Than 5x105, Measured …
Ultra-Abrupt Tapered Fiber Mach-Zehnder Interferometer Sensors, B. Li, J. Lan, W. Sumei, L. Zhou, Hai Xiao, Hai-Lung Tsai
Ultra-Abrupt Tapered Fiber Mach-Zehnder Interferometer Sensors, B. Li, J. Lan, W. Sumei, L. Zhou, Hai Xiao, Hai-Lung Tsai
Electrical and Computer Engineering Faculty Research & Creative Works
A fiber inline Mach-Zehnder interferometer (MZI) consisting of ultra-abrupt fiber tapers was fabricated through a new fusion-splicing method. By fusion-splicing, the taper diameter-length ratio is around 1:1, which is much greater than those (1:10) made by stretching. The proposed fabrication method is very low cost, 1/20-1/50 of those of LPFG pair MZI sensors. The fabricated MZIs are applied to measure refractive index, temperature and rotation angle changes. The temperature sensitivity of the MZI at a length of 30 mm is 0.061 nm/°C from 30-350 °C. The proposed MZI is also used to measure rotation angles ranging from 0° to 0.55°; …
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
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 …
Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary …
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 …
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear …
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. …
Impact-Induced Damage Characterization Of Composite Plates Using Neural Networks, Steve Eugene Watkins, Farhad Akhavan, Rohit Dua, K. Chandrashekhara, Donald C. Wunsch
Impact-Induced Damage Characterization Of Composite Plates Using Neural Networks, Steve Eugene Watkins, Farhad Akhavan, Rohit Dua, K. Chandrashekhara, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Impact-induced damage in fiber-reinforced laminated composite plates is characterized. An instrumented impact tower was used to carry out low-velocity impacts on thirteen clamped glass/epoxy composite plates. A range of impact energies was experimentally investigated by progressively varying impactor masses (holding the impact height constant) and varying impact heights (holding the impactor mass constant). The in-plane strain profiles as measured by polyvinylidene fluoride (PVDF) piezoelectric sensors are shown to indicate damage initiation and to correlate to impact energy. Plate damage included matrix cracking, fiber breakage, and delamination. Electronic shearography validated the existence of the impact damage and demonstrated an actual damage …
Incorporating The Effects Of Magnetic Saturation In A Coupled-Circuit Model Of A Claw-Pole Alternator, Hua Bai, Steven Pekarek, Jerry L. Tichenor, Walter Eversman, Duane J. Buening, Gregory R. Holbrook, Ronald J. Krefta
Incorporating The Effects Of Magnetic Saturation In A Coupled-Circuit Model Of A Claw-Pole Alternator, Hua Bai, Steven Pekarek, Jerry L. Tichenor, Walter Eversman, Duane J. Buening, Gregory R. Holbrook, Ronald J. Krefta
Electrical and Computer Engineering Faculty Research & Creative Works
A method of representing the effects of magnetic saturation in a coupled-circuit model of a claw-pole alternator is presented. In the approach considered, the airgap flux density produced by each winding is expressed as a function of magnetic operating point. A challenge in the implementation is that the airgap flux densities consist of several significant harmonics, each of which changes at a distinct rate as iron saturates. Despite this complication, it is shown that relatively simple measurements can be used to determine model parameters. The model is implemented in the analysis of several alternator/rectifier systems using a commercial state-model-based circuit …
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. …
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 …
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; …
Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller
Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller
Electrical and Computer Engineering Faculty Research & Creative Works
Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping …
Interdisciplinary Graduate Experience: Lessons Learned, Steve Eugene Watkins, V. M. Eller, Josh Corra, M. J. Molander, Bethany Konz, Richard H. Hall, K. Chandrashekhara, Abdeldjelil Belarbi
Interdisciplinary Graduate Experience: Lessons Learned, Steve Eugene Watkins, V. M. Eller, Josh Corra, M. J. Molander, Bethany Konz, Richard H. Hall, K. Chandrashekhara, Abdeldjelil Belarbi
Electrical and Computer Engineering Faculty Research & Creative Works
Engineers interact in the workplace with technical peers in other disciplines at all stages of design, development, and application. Awareness of the constraints and needs of the other disciplines can be key in many situations. Such interdisciplinary activity and the associated communication are facilitated if the all participants have a solid knowledge of discipline-specific terminology and an understanding of connecting concepts. Consequently, experience relating to interdisciplinary teamwork is a necessary component of engineering education. The Smart Engineering Group at the University of Missouri-Rolla was established to conduct interdisciplinary research and to create interdisciplinary educational resources. The topical interest area is …
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a …
Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan
Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan
Electrical and Computer Engineering Faculty Research & Creative Works
Artificial neutral networks (ANN) can be used as an online health monitoring systems (involving damage assessment, fatigue monitoring and delamination detection) for composite structures owing to their inherent fast computing speeds, parallel processing and ability to learn and adapt to the experimental data. The amount of impact-induced strain on a composite structure can be found using strain sensors attached to composite structures. Prior work has shown that strain-based ANN can characterize impact energy on composite plates and that strain signatures can be associated with damage types and severity. This paper reports the extension of this approach for damage classification using …
Process Modeling, Monitoring And Control Of Laser Metal Forming, N. Munjuluri, Sanjeev Agarwal, Frank W. Liou
Process Modeling, Monitoring And Control Of Laser Metal Forming, N. Munjuluri, Sanjeev Agarwal, Frank W. Liou
Electrical and Computer Engineering Faculty Research & Creative Works
Laser Metal Forming (LMF) process is one of the prominent Rapid Prototyping (RP) process that can be used to develop functional and fully dense metal parts. This paper addresses process modeling, monitoring and control of a laser metal forming system currently under development at Laser Aided Manufacturing Processes (LAMP) laboratory at University of Missouri--Rolla. This LMF system is based on a 2.5kW Nd:YAG laser as energy source and integrates five axis metal deposition and five axis machining. The current paper is aimed at characterization of effects of operating parameters such as traverse speed, mass flow-rate and laser power on the …
System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao
System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents multidisciplinary research and curriculum efforts at the University of Missouri-Rolla in the smart structures area. The primary objective of our project is to integrate research results with curriculum development for the benefit of students in electrical, and mechanical and aerospace engineering and engineering mechanics. The approach to the accomplishment of curriculum objectives is the development of a two-course sequence in the smart structures area with an integrated laboratory. The research portion of the project addresses structural identification and robust control methods for smart structures. A brief summary of the research results and a description of curriculum development …