bluuebunny/crossref_metadata_2025 huggingface dataset:
Defining Shapeability in Eigenstate Specification for Linear Systems
Authors: Saunders Frank , Rife Jason
Published: May 2012

This paper introduces a metric called degree of shapeability to determine whether or not it is possible to assign a set of desired eigenstates to a particular linear dynamic system. Specifying an eigenstate, which consists of an eigenvalue and its associated eigenvector, can be essential in designing certain vibrating structures and undulating mechanisms. In general it may be desirable to specify more than one eigenstate. The number of eigenstates that can be specified for a particular linear system is not always immediately evident, however, because of physical and kinematic constraints, as well as actuator placement. To quantify the potential for active feedback and/or passive parameter tuning to influence a system’s eigenstates, we identify a metric, the degree of shapeability, that relates the structure of the linear system to the number of eigenstates that a designer may fully define. We investigate the effect of system equality and inequality constraints on the ability to specify eigenstates for linear dynamic systems.
Nonlinear Robust H∞ Control With State-Dependent Scaling
Authors: Hays Scott , Wu Fen
Published: May 2012

This paper addresses the design of nonlinear robust H∞ controllers for nonlinear uncertain systems with polynomial vector field and norm bounded uncertainties. We derive state-dependent matrix inequalities, using Lyapunov’s direct method, that stabilize the nonlinear systems and guarantee robust performance using nonlinear state-feedback control. The state-dependent synthesis conditions incorporate state-dependent scaling to minimize the ℒ2 gain of the disturbance/output. Sum-of-squares (SOS) optimization is applied to solve the resulting synthesis condition with optimized ℒ2 gain for the nonlinear system, without requiring an iterative approach. Finally, a design example of nonlinear Van der Pol equation is presented.
Optimal Control for the Series-Parallel Displacement Controlled Hydraulic Hybrid Excavator
Authors: Zimmerman Joshua , Hippalgaonkar Rohit , Ivantysynova Monika
Published: May 2012

In this paper a hydraulic hybrid system architecture for multi-actuator displacement controlled systems is analyzed. In particular the problem of optimal control for a hybrid excavator with four actuators is solved. The system states and controls are identified and classified into those which are cycle defined and those which are free to vary during the duty cycle. A state space model is derived for the hybrid system using the free states and controls and an outline of the algorithm used to apply dynamic programming to the system is described. The optimal controls and states for an aggressive truck loading cycle of the excavator are compared with suboptimal controls and states obtained using a rule based control strategy. Finally a comparison is made for the simulated fuel consumption of the system using optimal and suboptimal controls. A comparison is also made between the fuel consumption of the hybrid and non-hybrid excavators.
Physically-Based Volumetric Efficiency Model for Diesel Engines Utilizing Variable Intake Valve Actuation
Authors: Kocher Lyle , Koeberlein Ed , Van Alstine D. G. , Stricker Karla , Shaver Greg
Published: May 2012

Advanced diesel engine architectures employing flexible valve trains enable emissions reductions and fuel economy improvements. Flexibility in the valve train allows engine designers to optimize the gas exchange process in a manner similar to how common rail fuel injection systems enable optimization of the fuel injection process. Modulating valve timings directly impacts the volumetric efficiency of the engine. In fact, the control authority of valve timing modulation over volumetric efficiency is three times larger than that due to any other engine actuator. Traditional empirical or regression-based models for volumetric efficiency, while suitable for conventional valve trains, are therefore challenged by flexible valve trains. The added complexity and additional empirical data needed for wide valve timing ranges limit the usefulness of these methods. A physically-based volumetric efficiency model was developed to address these challenges. The model captures the major physical processes occurring over the intake stroke, and is applicable to both conventional and flexible valve trains. The model inputs include temperature and pressure in the intake and exhaust manifolds, intake and exhaust valve timings, bore, stoke, connecting rod length, engine speed and effective compression ratio, ECR. The model is physically-based, requires no regression tuning parameters, is generalizable to other engine platforms, and has been experimentally validated using an advanced multi-cylinder diesel engine equipped with a flexible variable intake valve actuation system. Experimental data was collected over a wide range of the operating space of the engine and augmented with air handling actuator and intake valve timing sweeps to maximize the range of conditions used to thoroughly experimentally validate the model for a total of 217 total operating conditions. The physical model developed differs from previous physical modeling work through the novel application of ECR, incorporation of no tuning parameters and extensive validation on unique engine test bed with flexible intake valve actuation.
Laser Pulse Shaping via Iterative Learning Control and High-Dimensional Extremum Seeking
Authors: Ren Beibei , Frihauf Paul , Krstic Miroslav , Rafac Robert J.
Published: May 2012

We investigate pulse shaping and optimization for a laser amplifier. Due to the complex character of the nonlinear PDE dynamics involved in the laser model, it is of interest to consider non-model based methods for pulse shaping. We determine input pulse shapes for an unknown laser dynamics model using iterative learning control (ILC) and high-dimensional extremum seeking (ES), which is a real-time optimization strategy. We utilize ILC to obtain the input pulse shape that generates a desired output pulse shape and ES to find the input pulse that maximizes the energy amplifier gain. Both single-pass and double-pass laser models are investigated. The effectiveness of these approaches is illustrated via numerical simulations.
A Reduced-Order Electrochemical Model of Lithium-Ion Cells for System Identification of Battery Aging
Authors: Marcicki James , Rizzoni Giorgio , Conlisk A. T. , Canova Marcello
Published: May 2012

Lithium-ion batteries continue to garner interest as an energy storage system in stationary and vehicular applications. Considerable research effort is currently devoted to investigating the physical and chemical phenomena leading to aging, namely internal resistance growth and capacity fade. This paper presents a reduced-order model that characterizes the dynamic behavior of a Lithium-ion battery cell. The model is derived from the governing electrochemical principles and is applied to a Li-ion cell based upon a natural graphite negative electrode and iron phosphate positive electrode. The paper describes the modeling approach and equations, followed by a validation with experimental data. A sensitivity analysis is then conducted to investigate the influence of the model parameters on the cell internal resistance and capacity. The results of this study allows one to identify a subset of model parameters that may evolve throughout the battery’s life, providing guidance towards establishing which parameter trajectories must be quantified as batteries age.
An Exploratory Optimization Plus Kalman Filtering Based Method for Parameter Estimation in Model Based Diagnostics
Authors: Rengarajan Sankar B. , Bryant Michael D. , Choi Jaewon
Published: May 2012

Physics based models can be used to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. Residuals contain valuable information to interpret system states and parameters. A framework for parameter estimation and system identification of non-linear dynamical systems is presented with focus on DC motors and 3-phase induction motors. Tuning combines artificial intelligence techniques like Quasi-Monte Carlo sampling (Hammersley sequencing) and Genetic Algorithm (NSGA II) with an Extended Kalman filter (EKF) that utilizes the system dynamics information via physical models. A tentative Graphical User Interface (GUI) simplified interactions between machine operator and module. Implementation details and results comparing healthy and faulty systems are included.
SI and HCCI Combustion Mode Transition Control of a Multi-Cylinder HCCI Capable SI Engine via Iterative Learning
Authors: Yang Xiaojian , Zhu Guoming G.
Published: May 2012

The combustion mode transition between spark ignition (SI) and homogeneously charged compression ignition (HCCI) combustions of an internal combustion (IC) engine is challenging due to the distinct engine operational parameters over these two combustion modes and the cycle-to-cycle residue gas dynamics of the HCCI combustion. The control problem becomes even more complicated when multi-cylinder operation is involved. This paper studies the combustion mode transition problem of a multi-cylinder IC engine with dual-stage valve lifts and electrical variable valve timing systems. A control oriented engine model was used to develop a multistep mode transition control strategy via iterative learning for combustion mode transition between SI to HCCI with minimal engine torque fluctuations. The hardware-in-the-loop (HIL) simulations demonstrated the effectiveness of the developed control strategy for the combustion mode transition under both constant load and transient engine operational conditions.
Modeling and Control of Feed Drive System for a Micro-Machining Platform With Nano-Resolution
Authors: Ji Yingfeng , Zhao Ran , Xu Chengying
Published: May 2012

The feed drive system plays a key role of increasing processing efficiency and improving machining quality of parts. The external disturbances, such as cutting forces and frictional nonlinearities seriously affect the machining quality. The feed drive system should be capable of rejecting such disturbances. In this paper, the feed drive system of a micro-machining platform with nano-resolution was modeled and controlled. A decoupled algorithm was implemented to linearly regulate flux-producing and force-producing currents of motor. The current controller was designed based on the electrical model of motor employing pole-zero cancellation method. The speed controllers, i.e., proportional-integral (PI) and sliding mode control (SMC) were designed and compared. The SMC exhibits the better property of rejecting the external disturbance compared to PI controller. The electromagnetic force produced by Linear Permanent Magnet Synchronous Machine (LPMSM) under SMC expresses the quicker response to external variations. The models will be further applied for the design of cross-coupling servo control for this five-axis reconfigurable micro-machining platform.
Model Predictive Control of a Microgrid With Plug-In Vehicles: Error Modeling and the Role of Prediction Horizon
Authors: Peters Diane L. , Mechtenberg Abigail R. , Whitefoot John , Papalambros Panos Y.
Published: May 2012

We demonstrate the use of model predictive control (MPC) for a microgrid with plug-in vehicles. A predictive model is developed based on a hub model of the microgrid, and the control is optimized for minimum generator fuel usage. A variety of horizons and levels of prediction error are used in the optimization. A new method to model expected load and error is presented based on radial basis functions. Results show that for a given prediction horizon, as the level of prediction error increases, the amount of fuel used increases. Results also show that in some cases there is little benefit in extending the prediction horizon. While an extended prediction horizon does result in increased use of battery storage, this does not necessarily produce significant decreases in fuel usage. This result is analyzed and explained in terms of battery charging and discharging limitations.
Speed Improvement in Electrical Capacitance Tomography Through a Multiple Excitation and Receiving Method
Authors: Fan Zhaoyan , Gao Robert X.
Published: May 2012

Electrical Capacitance Tomography (ECT) is a method to determine the material distribution within the interior of a closed object by measuring the capacitance values across externally mounted electrodes. Traditionally, an AC excitation pulse is applied to a pair of electrodes that form a capacitor during each measurement step, in order to determine the capacitance from the output current measured. This paper investigates how the speed of inter-electrode capacitance measurement can be improved by comparatively studying three methods that affect the way electrodes are excited and signals are received: 1) multiple-excitation-single-receiving, 2) single-excitation-multiple-receiving, and 3) multiple-excitation-multiple-receiving. A PSPICE circuit model was built to simulate the bandwidth and interference between the simultaneously sampled measurement channels. Simulations using ECT sensors with 8 and 12-electrodes have shown that measurement speed can be increased by up to 3 ∼ 30 times as compared to the traditional mode of capacitance measurement. Such new capability opens up new possibilities for ECT as an effective tool for online, real-time monitoring of a wide range of dynamical processes in the industry.
Stochastically Resilient Observer Design for a Class of Discrete-Time Nonlinear Systems
Authors: Jeong Chung Seop , Yaz Edwin E. , Yaz Yvonne I.
Published: May 2012

A class of discrete-time nonlinear system and measurement equations having incrementally conic nonlinearities and finite energy disturbances is considered. A linear matrix inequality based nonlinear observer design approach is presented, which guarantees the satisfaction of a variety of performance criteria ranging from simple estimation error boundedness to dissipativity in the presence of random perturbations, due possibly to computational errors or changes during operation, on the observer gain. Some simulation examples are included to illustrate the proposed design methodology.
Closed Loop Stability of Switching Controller for Wind Turbine
Authors: Palejiya Dushyant , Chen Dongmei
Published: May 2012

Wind power intermittency due to wind speed variations can be alleviated by using a rechargeable battery as the reserve power source. In such an integrated wind turbine-battery system, wind turbine controller is frequently required to switch between two control modes. We have derived linearized approximations of the closed loop wind turbine system for both control modes after applying feedback control laws. Stability of this linearized switching system is established with the use of Common Quadratic Lyapunov Function (CQLF). Limitations on control gains and switching conditions required to achieve system stability are discussed. Simulations confirming the system stability are also presented.
A Novel Method for CFD Analysis of a Two-Zone Mixing Model for HCCI Engines
Authors: McCuen Matthew J. , Sun Zongxuan , Zhu Guoming
Published: May 2012

Increasing need for clean and efficient energy conversion devices in the automotive sector has pushed technologies such as homogeneous charge compression ignition (HCCI) to the forefront of research. While it offers a number of major advantages in terms of efficiency and emissions, HCCI has significant challenges to overcome before it can be used in a production setting. Among those challenges is the need to control the start of combustion, and one method for doing that is to use variable valve timing to utilize hot, residual exhaust gas. Critical to these residual-affected methods is the role of mixing on the thermodynamic state. This work presents a method for validating a control-oriented two-zone mixing model for HCCI using CFD software. A method of discretizing, extracting, and analyzing the results of the CFD simulation is described, and it is compared with the two-zone model. There is strong correlation, but there is also evidence that the two-zone model needs to be further developed to fully capture the dynamics of mixing.
Hybrid Neural Net Model of a Lithium Ion Battery
Authors: Refai Rehan , Yayathi Sandeep , Chen Dongmei , Fernandez-Rodriguez Benito
Published: May 2012

This paper discusses the development of a hybrid neural net and physics based battery model. A control oriented one dimensional physics based model of a battery is developed. A neural net based modeling approach is used to compensate for the lack of knowledge of material parameters for the battery cell. Given the knowledge of the physics of the battery, sparse recurrent neural nets are used. Multiple types of standalone neural nets as well as hybrid neural net and physics based battery models are developed and tested to determine the appropriate configuration for an optimal performance. All neural nets are trained as open-loop (feed-forward) systems and are tested as recurrent systems, with the battery state of charge being fed back. The neural nets are trained, tested and validated using test data from a 4.4Ah Boston Power lithium ion battery cell. The modeling approach presented in this paper is able to accurately simulate battery performance for multiple current profiles.
Payload Parameter Real-Time Estimation for Lightweight Vehicles
Authors: Huang Xiaoyu , Wang Junmin
Published: May 2012

This paper proposes a payload parameter estimation method for lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs’ significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for controller designs and load condition monitoring. The proposed payload parameter estimator (PPE) is divided into two parts: tire nominal normal force estimator (NNFE) based on a recursive least squares (RLS) algorithm using signals measured from LWV constant speed maneuvers, and parameter calculator based on estimated nominal normal forces. The prototype LWV is an electric ground vehicle with separable torque control of the four wheels by in-wheel motors, which allow redundant input injections in the designed maneuvers. Simulation results, based on a CarSim® model, show that the proposed PPE is capable of accurately and quickly estimating payload parameters, and is independent of the road condition as long as the tire forces are kept within their linear ranges.
A New Biomechanical Model of Vocal Fold Vibrations: Preliminary Experimental Validation
Authors: Wei Zhenyi , de Queiroz Marcio , Chen Jing , Gunturk Bahadir K. , Kunduk Melda
Published: May 2012

This paper introduces a new seven-mass biomechanical model for the vibration of vocal folds. The model is based on the body-cover layer concept of the vocal fold biomechanics, and segments the cover layer into three masses. This segmentation facilitates the model comparison with the motion of the vocal glottis contour derived from modern high-speed digital imaging systems. The model simulation is compared to experimental data from a pair of healthy vocal folds showing good agreement in the frequency and time domains. The proposed model is also shown to outperform a previously-developed model that does not explicitly account for the body layer dynamics.
On the Influence of Composition on the Thermally-Dominant Recompression HCCI Combustion Dynamics
Authors: Jade Shyam , Hellstro¨m Erik , Stefanopoulou Anna , Jiang Li
Published: May 2012

A zero dimensional, mean-value, control-oriented model for recompression homogeneous charge compression ignition (HCCI) combustion with two discrete states representing temperature and composition dynamics is presented. This model captures steady state magnitudes and trends in combustion phasing, residual gas fraction, and mass flows caused by sweeps in valve timings, fueling rate, and fuel injection timing. It is shown that the coupling of the composition state with the mainly thermally-driven combustion dynamics causes competing slow and fast dynamics that shape the transient response of the phasing. A decoupled version of the model, where composition does not affect combustion phasing, is also developed in an effort to further simplify the model. This version matches the steady state fidelity of the coupled model, but has a qualitatively different dynamical behavior. Both models exhibit complex behaviors such as limit cycles at extremely late phasing. Both realizations are valid contenders as low order steady state representations of HCCI behavior. High-fidelity transient data will be necessary to further clarify the necessity of including composition effects on combustion phasing.
Influence of Leaders on Mean Square Consentability in Biologically-Inspired Stochastic Networks
Authors: Abaid Nicole , Porfiri Maurizio
Published: May 2012

In this work, we study a discrete-time consensus protocol for a group of agents which communicate over a class of stochastically switching networks inspired by fish schooling. The network model incorporates the phenomenon of numerosity that has a prominent role on the collective behavior of animal groups by defining the individuals’ perception of numbers. The agents comprise leaders, which share a common state, and followers, which update their states based on information exchange among neighboring agents. We write a closed form expression for the asymptotic convergence factor of the protocol, which measures the decay rate of disagreement among the followers’ and the leaders’ states. Numerical simulations are conducted to validate analytical results and illustrate the consensus dynamics as a function of the group size, number of leaders in the group, and the numerosity.
Responsible-Eigenvalue Control for Creating Autonomy in Coupled Systems With Delays
Authors: Qiao Wei , Sipahi Rifat
Published: May 2012

A class of linear time-invariant (LTI) consensus system with multiple agents and identical communication delays among the agents is considered. For this type of system, we recently showed that a responsible eigenvalue (RE) exists determining the amount of delay (delay margin) that the system can withstand without losing stability. In this paper, we use RE to design controllers such that the delay margin of the system increases as the agents make autonomous decisions. This Responsible Eigenvalue-control enables real-time tuning of RE in a way that the system becomes robust against delays. A numerical example demonstrates intriguing results that connect for the first time in the literature the controller design, the features of the associated graph Laplacian, and RE concept.
Power Flow Control in Hybrid Electric Vehicles
Authors: Becerra Guillermo , Mendoza-Soto Jose´ Luis , Alvarez-Icaza Luis
Published: May 2012

In this paper a new strategy for controlling the power flow in hybrid electric vehicles is described. The strategy focuses in the planetary gear system where kinematic and dynamic constraints must be satisfied. The aim is to satisfy driver demands and to reduce fuel consumption. The resultant power flow control is continuous and uses the internal combustion engine with the maximum possible efficiency. The strategy is not optimal, although it is inspired by the solution to most optimization problems. The main advantages are that the computational cost is low, when compared to optimization based approaches, and that it is easy to tune. The strategy is tested with simulations using a mathematical model of a power train of a hybrid diesel-electric bus subjected to the power demands of representative urban area driving cycles. Simulation results indicate that the strategy achieves small speed tracking errors and attains good fuel consumption reduction levels.
The Synergistically Propelled Ichthyoid: Dynamic Modeling, Simulation, and Experimental Results
Authors: Strefling Paul C. , Hellum Aren M. , Mukherjee Ranjan
Published: May 2012

We have developed a novel type of submersible called a Synergistically Propelled Ichthyoid [SPI] which is propelled by the combined jet and tail action of a fluttering fluid-conveying pipe. Two dynamic models for an SPI are presented and solved; an analytically tractable model in an inertial reference frame and a more complex model in a general planar reference frame. The latter model has been solved numerically—these simulations show a benefit to using a fluttering tail relative to a dimensionally identical rigid tail. Construction details of an experimental realization are provided. Preliminary measurements taken using that platform are also provided which qualitatively confirm the simulation’s conclusion that a fluttering flexible tail is capable of higher speed than a dimensionally identical rigid tail.
Real-Time and Robust Estimation of Biodiesel Blends Based on Fuel Consumption and Crankshaft Torsionals
Authors: Mirheidari S. , Franchek M. A. , Grigoriadis K. M. , Mohammadpour J. , Wang Y. Y. , Haskara I.
Published: May 2012

Biodiesel is a renewable alternative fuel which produces lower exhaust emissions compared to its petroleum-based counterpart, diesel. While all of the emissions are less for biodiesel compared to diesel fuel, nitrogen oxides (NOx) increases. To meet the emissions requirements by engine manufacturers, information about biodiesel fuel must be provided for engine control system to be able to compensate the increase in NOx emissions caused by using biodiesel. Moreover, biodiesel blend information can be useful to improve performance on the biodiesel fueled engine. Biodiesel blend estimation based on fuel consumption and produced engine torque is investigated in this paper. This strategy is fitted in a robust adaptive estimation structure for achieving a robust estimation. Sensitivity analysis has been done to check the robustness of the estimators against measurement errors.
Performance Evaluation of a Triangular-Prism-Slit Electrode Pair as an Electro-Conjugate Fluid Jet Generator
Authors: Wang Hai-bo , Kim Joon-wan , Yokota Shinichi , Edamura Kazuya
Published: May 2012

Electro-conjugate fluid (ECF) is a dielectric and functional fluid, which generates a powerful jet when electrodes inserted into it are subjected to a constant voltage of less than one thousand volts. As one essential research field on ECF, researchers have been conducting the study on electrodes. Several structures, e.g. planar parallel rod-like electrode array, ring-needle electrode pair and triangular-prism-slit (TPS) electrode pair, were proposed. Among them, the TPS electrode structure is often thought as the most promising candidate for future ECF applications thanks to its great merits of combining easy fabrication and relatively high performance. In this paper, in order to evaluate performance of the TPS electrode pair, a novel modular ECF-jet generator capable of independently adjusting alignment and gap of each electrode is designed. By utilizing it, the relationship between output pressure and parameters of the TPS electrode pair, including thickness, slit width, tip angle, electrode gap and alignment, are obtained.
Terrain Gridding Using a Stochastic Weighting Function
Authors: Ma Rui , Ferris John B.
Published: May 2012

The development of new stochastic terrain gridding methods are necessitated by new tire and vehicle modeling applications. Currently, grid node locations in the horizontal plane are assumed to be known and only the uncertainty in the vertical height estimates is modeled. This work modifies the current practice of weighting the importance of a particular measured data point (the terrain height at some horizontal location) by the inverse distance between the grid node and that point. A new weighting function is developed to account for the error in the horizontal position of the grid nodes. The geometry of the problem is described and the probability distribution is developed in steps. Although the solution cannot be determined in closed form, an estimate of the median distance is developed within 1% error. This more complete stochastic definition of the terrain can then be used for advanced tire modeling and vehicle simulation.
Dynamics and Control of the Reaction Mass Pendulum (RMP) as a 3D Multibody System: Application to Humanoid Modeling
Authors: Sanyal Amit K. , Goswami Ambarish
Published: May 2012

Humans and humanoid robots are often modeled with different types of inverted pendulum models in order to simplify the dynamic analysis of gait, balance and fall. We have earlier introduced the Reaction Mass Pendulum (RMP), an extension of the traditional inverted pendulum models, which explicitly captures the variable rotational inertia and angular momentum of the human or humanoid. In this paper we present a thorough analysis of the RMP, which is treated as a 3D multibody system in its own right. We derive the complete kinematics and dynamics equations of the RMP system and obtain its equilibrium conditions. Next we present a nonlinear control scheme that stabilizes this underactuated system about an unstable set with a vertically upright configuration for the “leg” of the RMP. Finally we demonstrate the effectiveness of this controller in simulation.
Impact-Based Modeling of Backlash in Gear Driven Systems
Authors: Vasudevan Hari , Morrell John
Published: May 2012

Backlash in gears creates nonlinear dynamics in mechanical systems and has been analyzed using a variety of methods and models. In this paper, we present a nonlinear, piecewise model for backlash dynamics that incorporates a coefficient of restitution to account for impact behavior during limit cycling. We analyze the energy absorbed by the collisions and derive a constraint for feedback controllers. We conclude with simulation results using this modeling approach.
Thermodynamics-Based Optimization and Control of Vapor-Compression Cycle Operation: Control Synthesis
Authors: Jain Neera , Alleyne Andrew G.
Published: May 2012

This paper considers the implementation of an exergy-based multiple degree of freedom (MDOF) optimization and control methodology for the operation of VCC systems. The optimization problem for the standard VCC is characterized in terms of 4 thermodynamic variables and 1 fluid-dynamic variable. The resulting control problem is then analyzed, and a design variable, Λ, is introduced which allows the user to choose how the optimization variables are projected onto a control space of lower dimension. The potential of this approach to improve operational efficiency, with respect to both first and second law efficiency metrics, is demonstrated on an experimental VCC system through implementation of the proposed optimization using a feedforward plus feedback control architecture.
Maximum Power Point Tracking for Photovoltaic Systems Using Adaptive Extremum Seeking Control
Authors: Li Xiao , Li Yaoyu , Seem John E. , Lei Peng
Published: May 2012

Due to the relatively higher cost of energy (COE) for the photovoltaic (PV) systems, it is crucial to locate the maximum power point (MPP) so as to increase the system efficiency. The nonlinear PV characteristic curve and the MPP depend on PV’s intrinsic characteristics and environment conditions such as solar irradiation intensity and temperature. Maximum power point tracking (MPPT) control serves to seek the MPP of the PV system with the unpredicted environment uncertainties. In this paper, the adaptive extremum seeking control (AESC) scheme is investigated for the PV MPPT, which optimizes the duty ratio for the pulse-width modulator (PWM) of the DC-DC converter. The adopted AESC scheme utilizes an explicit structure information of the PV-buck system based on the system states and unknown PV characteristics. The radial basis function (RBF) neural network has been used to approximate the unknown nonlinear I-V curve. A Lyapunov-based adaptive learning control technique is used to ensure the convergence of the system to a neighborhood of the optimum which depends on the approximation error. The performance of the controller is verified through simulation.
H
2 Guaranteed Cost Control Design and Implementation for Dual-Stage Hard Disk Drive Track-Following Servos
Authors: Nie Jianbin , Conway Richard , Horowitz Roberto
Published: May 2012

This paper discusses the design and implementation of H2 guaranteed cost control for dual-stage hard disk drive track-following servo systems. The proposed approach is based on H2 guaranteed cost analysis, in which an upper bound on the worst-case H2 performance of a discrete-time system with gain-bounded unstructured uncertainty is determined via several Riccati equations. Subsequently, the output feedback H2 guaranteed cost control synthesis algorithm is presented by exploiting Riccati equation structure to reduce the number and complexity of the semi-definite programs (SDPs) that need to be solved. The presented control synthesis methodology is then applied to a hard disk drive with a PZT-actuated suspension. Experimental results on the actual disk drive validate our control design.
An Approximation Algorithm for a Heterogeneous Traveling Salesman Problem
Authors: Bae Jungyun , Rathinam Sivakumar
Published: May 2012

Surveillance applications require a collection of heterogeneous vehicles to visit a set of targets. In this article, we consider a fundamental routing problem that arises in these applications involving two vehicles. Specifically, we consider a routing problem where there are two heterogeneous vehicles that start from distinct initial locations, and a set of targets. The objective is to find a tour for each vehicle such that each of the targets is visited at least once by a vehicle and the sum of the distances traveled by the vehicles is a minimum. We present a novel primal-dual algorithm for the same that provides an approximation ratio of 2.
Flatness-Based Control of Flexible Motion Systems
Authors: Post Brian K. , Mariuzza Alexandre , Book Wayne J. , Singhose William
Published: May 2012

Flexibility is often an unavoidable limitation when large-workspace high-speed manipulation is required. This flexibility can be mitigated in some circumstances through feedback control methods. However, these methods only correct for vibration after it has been measured. Therefore, if low-vibration reference commands can be generated, then the utility of these systems can be greatly improved. However, there are instances where system nonlinearities limit the effectiveness of many command-shaping techniques. This paper proposes a method for the generation of fast vibration-limiting trajectories for flexible systems based on the differential-flatness property of nonlinear systems. This approach is applied to a tower crane for simulation and experimental validation. The results are compared to those from standard command-shaping techniques. Practical implementation issues for real world systems are discussed.
Micropositioning of a Multimaterial Electrohydrodynamic Jet Deposition System Using Vision Feedback
Authors: Sutanto Erick , Hoelzle David J. , Alleyne Andrew G. , Shigeta Kazuyo , Rogers John A.
Published: May 2012

This paper presents the micropositioning of a multimaterial Electrohydrodynamic-Jet (E-jet) deposition system with vision feedback. E-jet printing has been recognized as an emerging direct write technology for micro/nano-manufacturing applications. Recent efforts have improved E-jet droplet resolution and printing speed. The next logical step is to increase the versatility of the printing system by increasing the number of materials that can be integrated to a single devise. Droplet resolution using E-jet deposition is generally less than 5μm in diameter, so it is necessary to accurately position the nozzle tip after successive head switching. Here, we divide the switching mechanism into two steps: macropositioning and micropositioning. The micropositioning is performed with a servo control with machine vision, resulting in a positioning accuracy of less than 2 μm. The efficacy of this approach is demonstrated through printing of a multimaterial pattern.
Formation Control of Platoons of Mobile Sensors With Intermittent Communications
Authors: Spinello Davide , Stilwell Daniel J.
Published: May 2012

We present a fully decentralized motion control algorithm for coordination of platoons of mobile agents with restricted communication capabilities. In order to address very low bandwidth communication between agents and time varying communication network topologies, we utilize a distributed full-state estimator onboard each agent. Agent motion and data fusion algorithms are implemented locally by each agent. We introduce a gradient-based framework in which we achieve asymptotic agreement among local (estimated) representations of the platoon and the actual configuration in terms of outputs of the agents.
The Application of Bioinspired Jumping Locomotion Principles to Mobile Robots: Modeling and Analysis
Authors: Gilani Omar , Ben-Tzvi Pinhas
Published: May 2012

Nature provides various alternative locomotion strategies which could be applied to robotic systems. One such strategy is that of jumping, which enables centimeter to millimeter-scaled insects to traverse highly unstructured environments quickly and efficiently. These insects generate the required high magnitude power through specialized structures which store and rapidly release large amounts of energy. This paper presents an investigation into the morphology of natural jumpers and derives a generalized mathematical model based on them. The model describes mathematically the relationships present in a jumping system which uses a pause-and-leap jumping strategy. The use of springs as energy storage elements for such a jumping system is assessed. The discussion is then further extended to another bioinspired approach that can be applied to a jumping robot: that of gliding using foldable wings. The developed jumping and gliding mobility paradigm is analyzed and its feasibility for mobile robot applications is discussed.
Active Compressor Surge Control Using Piston Actuation
Authors: Uddin Nur , Gravdahl Jan Tommy
Published: May 2012

A novel approach to active surge control in compressors using piston actuation is presented. Two control laws are compared in order to evaluate the feasibility of implementing the concept. The first control law is a nonlinear feedback control derived by using backstepping and the second one is a linear feedback control derived by analyzing the eigenvalues of the linearized system around the operating point. The nonlinear feedback control law makes the closed loop system globally asymptotically stable (GAS) and uses full states feedback. The linear feedback control is only using feedback from plenum pressure and piston velocity and the removal of the mass flow feedback is advantageous for implementation. The closed loop system with the linear feedback control is locally asymptotically stable around the operating point. Simulations show that both controllers are capable of stabilizing surge.
Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle: Influence of Inclusion of Powertrain Dynamics
Authors: Borhan Hoseinali , Vahidi Ardalan , Liang Wei , Phillips Anthony , Di Cairano Stefano , Kuang Ming , McGee Ryan
Published: May 2012

This paper builds on our previous published works in which we had employed nonlinear model predictive control for the (sub)optimal power management of a power-split hybrid electric vehicle (HEV). In addition to the battery’s state of charge, in this work we include the effect of inertial powertrain dynamics in the control-oriented model that are usually ignored because of their fast dynamics. We show how inclusion of the new state removes the need for a separate rule-based strategy for engine start/stop and can result in considerable improvement in the fuel economy as shown by closed-loop simulations over a high-fidelity power-split HEV model.
Operational Energy Optimization for Pure Electric Ground Vehicles Based on Terrain Profile Preview
Authors: Chen Yan , Chen Zili , Wang Junmin
Published: May 2012

This paper presents an operational energy optimization method for pure electric ground vehicles (EGVs) based on terrain profile preview. Dynamic programming is applied to obtain the global optimal energy point for torque distributions. The vehicles considered in this paper are powered by batteries and in-wheel motors. Different from the fuel optimization methods of conventional hybrid electric vehicles which adjust battery power supplies, a pure EGV actuated by in-wheel motors has only one energy source, whose power consumption is mainly determined by the torque distributions among in-wheel motors in terms of their operating efficiencies. Moreover, the optimization results show that different terrains will influence the optimal torque distributions and energy savings.
A Firing-Rate Lateral Amygdala Network Model With Calcium-Dependent Synaptic Learning
Authors: Ball John M. , Hummos Ali H. , Nair Satish S.
Published: May 2012

Neurons in the nervous system communicate by spiking, which activates synaptic connections via the release of neurotransmitter molecules. Modification of the strength of these synaptic connections, known as plasticity, is a mechanism by which networks of neurons can exhibit learning. Previously, a biophysical model of a rodent lateral amygdala was developed that could learn and store auditory fear and extinction memories following classical Pavlovian fear conditioning [1]. We propose a novel reduced order model that preserves the learning capabilities of the detailed model with considerably fewer computations while providing additional insights into the synaptic learning process. To capture the dynamics of individual cells, we propose enhancements to the Wilson-Cowan firing rate model that permit “full” spike frequency adaptation and a non-zero threshold. To incorporate synaptic learning dynamics, we propose a regression technique to capture the nonlinear relationship between firing rate and synaptic [Ca2+]. The resulting method provides a general technique to develop neuronal networks that employ [Ca2+]-dependent synaptic learning.
Controlling Vehicle Instability Through Stable Handling Envelopes
Authors: Beal Craig E. , Bobier Carrie G. , Gerdes J. Christian
Published: May 2012

Loss of control accidents result in thousands of fatalities in the United States each year. Production stability control systems are highly effective in preventing these accidents, despite their reliance on a hand-tuned response to data from a small set of sensors. However, improvements in sensing offer opportunities to determine stabilizing actions in a more systematic manner. This paper presents an approach that utilizes the yaw-sideslip phase plane to choose boundaries that eliminate unstable and undesirable driving regimes. These boundaries may be varied to obtain desirable performance and driver acceptance and form the basis for a driver assistance system that augments the driver input to maintain the vehicle within the bounds of a safe handling envelope. Experimental results from a model predictive controller used to enforce the envelope boundaries on a steer-by-wire vehicle are presented to demonstrate the viability of this framework for implementing stability boundaries.
A Hybrid of Impulsive and Continuous Control for Kneeless Bipedal Walking
Authors: Tavakoli Ali , Hurmuzlu Yildirim
Published: May 2012

The objective of this paper is to develop a simple active control scheme for a kneeless bipedal walking robot. Our control objective is to generate asymptotically stable walking gait pattern by impulsive control actions at the feet of the robot. Consequently, a nonlinear impulsive controller is designed for the hybrid dynamics of the system that can actively generate a wide range of desired ballistic walking gaits on a flat surface. Furthermore, the role of continuous control in the closed loop dynamics of the system is investigated. We show a combination of continuous and impulsive control can maintain a non-scuffing bipedal walking on arbitrary ground slope angles. Our results show that the controlled systems successfully generate active gaits without significant degradation in the energetic efficiencies of the equivalent passive systems.
High-Speed Nanomechanical Property Measurement in Liquid Using Inversion-Based Iterative Feedforward-Feedback Control
Authors: Zhang Yan , Zou Qingze
Published: May 2012

This article presents an inversion-based iterative feedforward-feedback (II-FF/FB) approach to achieve high-speed nanomechanical property measurement of soft materials in liquid. Measurement of nanomechanical properties in liquid is needed for studying a wide variety of soft materials, particularly live biological samples such as live cell. Moreover, high-speed nanomechanical property measurement is needed when dynamic evolution of the sample occurs and/or the rate-dependent viscoelasticity of materials is measured. Nanomechanical measurement in liquid, however, is challenged by the adverse effects including the thermal drift effect, the reduction of the signal to noise ratio, and the hysteresis and the vibrational dynamics effects of the piezoelectric actuators (used to position the probe relative to the sample). Thus, the main contribution of the article is the development of the II-FF/FB approach to tackle these challenges. The proposed method is illustrated through force-curve measurement on a poly (dimethylsiloxane) (PDMS) sample in liquid at high-speed. The experimental results demonstrated the efficacy of the proposed approach in achieving high-speed force-curve measurements of soft samples in liquid.
An Experimentally Tuned Dynamic Model Predicting Cell Migration for Guidance of Sprouting Endothelial Cells
Authors: Wood Levi , Asada H. Harry
Published: May 2012

Endothelial cells (ECs) create a vascular network with a tubular structure in response to growth factors diffused into the gel and interactions with the surrounding environment. Individual cells migrate in response to all of these cues, leading to network pattern formation. This paper presents a dynamic model predicting EC sprout growth that is tuned to time-lapse experimental cell migration data obtained from microfluidic 3D culture. Simple cell migration equations with just a few parameters are formulated and a Maximum Likelihood estimator is used for estimating model parameters from experimental data. The tuned model is used to evaluate the influence of different sprout elongation rates on cell density in the sprout stalk. This quantitative modeling approach will lead to input shaping and feedback control to optimize sprouting metrics such as stalk cell density.