Accepted Manuscripts

Elaheh Noursadeghi and Ioannis Raptis
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037839
This paper deals with the distributed fault detection and isolation problem of uncertain, nonlinear large-scale systems. The proposed method targets applications where the computation requirements of a full-order failure-sensitive filter would be prohibitively demanding. The original process is subdivided into low-order interconnected subsystems with, possibly, overlapping states. A network of diagnostic units is deployed to monitor, in a distributed manner, the low-order subsystems. Each diagnostic unit has access to a local and noisy measurement of its assigned subsystem's state, and to processed statistical information from its neighboring nodes. The diagnostic algorithm outputs a filtered estimate of the system's state and a measure of statistical confidence for every fault mode. The layout of the distributed failure-sensitive filter achieves significant overall complexity reduction and design flexibility in both the computational and communication requirements of the monitoring network. Simulation results demonstrate the efficiency of the proposed approach.
TOPICS: Algorithms, Design, Computation, Failure, Fault diagnosis, Filters, Flaw detection, Simulation results, Stochastic systems
Design Innovation Paper  
Pugi Luca, Allotta Benedetto, Boni Enrico, Guidi Francesco, Montagni Marco and Massai Tommaso
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037840
In order to control an autonomous sail-boat it's fundamental a correct estimation of both direction and intensity of wind velocity. This kind of measurements has to be performed in a quite harsh environment considering the direct exposition of the sensor to salt, fog and to any variable weather conditions. An important feature is represented by the sensor size, which has to be small compared to the drone size. Also costs have to be optimized respect to the overall small budget involved in the construction of the drone. Finally, extensive use on drones or in large sensors networks should be greatly advantaged by an easy substitutability in case of accidental damage or system loss, an eventuality which is difficult to be completely avoided for large scale, prolonged monitoring activities. In this work authors propose a low cost ultrasonic planar anemometer with a very interesting price to performance ratio which is obtained by introducing a simple, original and innovative Arduino based architecture. Preliminary design and the results of calibration will be described, followed by testing activities performed on a low-speed large section wind tunnel, available at University of Florence supported by simple but effective CFD simulations.
TOPICS: Testing, Design, Unmanned aerial vehicles, Sensors, Wind velocity, Simulation, Construction, Computational fluid dynamics, Calibration, Wind tunnels, Boats, Damage, Engineering simulation
Mitchel J. Craun and Bassam Bamieh
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037838
We develop a first-principles model of the regenerator component of a generic Stirling engine. The model is based on the Euler equations of one dimensional gas dynamics coupled with its convective/conductive heat transfer with the embedded mesh material. We investigate various methods for deriving simpler and low order control-oriented models from this first principles model. The basic criterion being high fidelity representation of the dynamics of the regenerator when coupled to other dynamic components of the engine. We identify several non-dimensional parameters that potentially categorize different modes of operation, and investigate the corresponding time-scale separation. A hierarchy of singularly perturbed models are derived in which acoustic dynamics are eliminated, periodic mesh dynamics are averaged, and the shape of the distributed regenerator gas state is approximated respectively. In addition, since the reduced model is to be operated cyclically when connected to other parts of the engine, we develop such a feedback-aware model reduction algorithm based on a Proper Orthogonal Decomposition (POD) with a chirped signal input (chirp-POD). This algorithm yields reduced models that are accurate over a range of engine operating frequencies.
TOPICS: Dynamics (Mechanics), Stirling engines, Modeling, Engines, Algorithms, Gasdynamics, Heat transfer, Separation (Technology), Acoustics, Feedback, Principal component analysis, Shapes, Signals
HE MA, Ziyang Li, Mohammad Tayarani, Guoxiang Lu, Hongming Xu and X. Yao
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037835
Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in OEMs and universities. The state-of-the-art model-based steady state Design of Experiments (DoE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy, and robust testing process with high data quality by high quality testing facility, also constrain the further development of model-based DoE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 GDI engine test bench, a Simulink real-time based embedded system was developed and implemented to engine ECU through Rapid Control Prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and PM emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model, or massive test bench experimental data. It has great potential to improve the engine calibration process for industries.
TOPICS: Internal combustion engines, Calibration, Engines, Experimental design, Steady state, Test facilities, Fuel consumption, Emissions, Direct injection spark ignition engines, Explosions, Testing, Embedded systems, Evolutionary algorithms
Singith Abeysiriwardena and Tuhin Das
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037837
The theoretical problem addressed in the present work involves the effect of integral feedback on a class of uncertain nonlinear systems. The intriguing aspects of the problem arise as a result of transient constraints combined with the presence of parametric uncertainty and an unknown nonlinearity. The motivational problem was the state-of-charge (SOC) control strategy for load-following in Solid Oxide Fuel Cells (SOFCs) hybridized with an ultra-capacitor. In the absence of parametric uncertainty, our prior work established asymptotic stability of the equilibrium if the unknown nonlinearity is a passive memoryless function. In contrast, this paper addresses the realistic scenario with parametric uncertainty. Here, an integral feed-back/parameter adaption approach is taken to incorporate robustness. The integral action, which results in a higher-order system, imposes further restriction on the nonlinearity for guaranteeing asymptotic stability. Furthermore, it induces a limit cycle behavior under additional conditions. The system is studied as a Lure problem, which yields a stability criterion. Subsequently, the describing function method yields a necessary condition for half-wave symmetric periodic solution (induced limit cycle).
TOPICS: Stability, Nonlinear systems, Feedback, Limit cycles, Uncertainty, Solid oxide fuel cells, Robustness, Stress, Waves, Equilibrium (Physics), Transients (Dynamics), Capacitors
Wellington A. Silva, Bismark C. Torrico, Wilkley B. Correia and Laurinda L. N. dos Reis
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037836
Many industrial and laboratory applications which make use of electric machines require non-interruption operation, even in the presence of faults, such as power generation and electric vehicles. Under fault scenarios the performance of the system is expected to degrade and control techniques may be helpful to overcome this issue. Within this context, phase faults are obviously undesired, as may lead the machine to stop operating. Switched Reluctance Machines (SRM), due its inherit characteristics, are naturally tolerant to phase faults, despite of the loss of performance. Most of the techniques used to improve the performance of SRMs in fault situations are related to the switching feed converter. Regarding this issue, instead of presenting an alternative converter topology, this work alternatively proposes a control approach which significantly reduces the phase faults effects on the speed of the motor. Furthermore, the high frequency noise is attenuated when compared to the classical PI controller, commonly applied to control such sort of motors. The proposed SRM-AFC controller is able to recover the speed of operation faster than a classical approach, when a feedforward action is not taken into account.
TOPICS: Feedforward control, Machinery, Control equipment, Motors, Noise (Sound), Energy generation, Electric vehicles, Topology
Technical Brief  
Gabriel Ingesson, Lianhao Yin, Rolf Johansson and Per Tunestal
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037834
The problem of designing robust and noise-insensitive PI controllers for pressure-sensor based combustion-timing control was studied through simulation. Different primary reference fuels (PRF) and operating conditions were studied. The simulations were done using a physics-based, control-oriented model with an empirical ignition-delay correlation. It was found that the controllable region, in-between the zero-gain region for early injection timings and the misfire region for late injection timings is strongly PRF dependent. As a result, it was necessary to adjust intake temperature to compensate for the difference in fuel reactivity prior to the controller design. With adjusted intake temperature, PRF dependent negative-temperature coefficient behavior gave different system characteristics for the different fuels. The PI-controller design was accomplished by solving the optimization problem of maximizing disturbance rejection and tracking performance subject to constraints on robustness and measurement-noise sensitivity. Optimal controller gains were found to be limited by the high system gain at late combustion timings and high-load conditions, furthermore, the measurement-noise sensitivity was found to be higher at the low-load operating points where the ignition delay is more sensitive to variations in load and intake-conditions. The controller-gain restrictions were found to vary for the different PRFs, the optimal gains for higher PRFs were lower due to a higher system gain, whereas the measurement-noise sensitivity was found to be higher for lower PRFs.
TOPICS: Combustion, Control equipment, Design, Diesel engines, Feedback, Heptane, Noise (Sound), Fuels, Temperature, Stress, Simulation, Ignition delay, Robustness, Optimization, Pressure sensors, Physics
Mahdi Abolhasani and Mehdi Rahmani
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037777
In this paper, a new robust Kalman filter is proposed for discrete-time time-varying linear stochastic systems. The system under consideration is subject to stochastic and norm-bounded uncertainties in all matrices of the system model. In the proposed approach, the filter is first achieved by solving a stochastic min-max optimization problem. Next, we find an upper bound on the estimation error covariance and then by using a linear matrix inequality optimization problem, unknown parameters of the filter are determined such that the obtained upper bound is minimized. Finally, two numerical examples are given to demonstrate the effectiveness and performance of the proposed filtering approach compared to the existing robust filters.
TOPICS: Filtration, Time-varying systems, Uncertainty, Filters, Optimization, Errors, Kalman filters, Linear matrix inequalities, Stochastic systems
Minh Q. Phan, Francesco Vicario, Richard W. Longman and Raimondo Betti
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037778
This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input-output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.
TOPICS: Kalman filters, Noise (Sound), Algorithms, Errors, Steady state
Chang Liu, Shengbo Li, Diange Yang and J. Karl Hedrick
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037779
This paper presents a novel distributed Bayesian filtering (DFB) method using measurement dissemination for multiple unmanned ground vehicles (UGVs) with dynamically changing interaction topologies. Different from statistics dissemination-based algorithms that transmit posterior distributions or likelihood functions, this method relies on a full-in-full-out (FIFO) transmission protocol, which significantly reduces the transmission burden between each pair of UGVs. Each UGV only sends a communication buffer and a track list to its neighbors, in which the former contains a history of measurements from all UGVs, and the latter trims the used measurements in the buffer to reduce communication overhead. It is proved that each UGV can disseminate its measurements over the whole network within the finite time and is able to achieve the consistency of environmental state estimation. The effectiveness of this method is validated by comparing with consensus-based distributed filters and centralized filter in a multi-target tracking problem.
TOPICS: Filters, Polishing equipment, Algorithms, Vehicles, State estimation, Statistics as topic, Filtration
Amirhossein Taghvaei, Jana de Wiljes, Prashant Mehta and Sebastian Reich
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037780
This paper is concerned with the filtering problem in continuous-time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman-Bucy filter which provides an exact solution for the linear Gaussian problem, (ii) the ensemble Kalman-Bucy filter (EnKBF) which is an approximate filter and represents an extension of the Kalman-Bucy filter to nonlinear problems, and (iii) the feedback particle filter (FPF) which represents an extension of the EnKBF and furthermore provides for a consistent solution in the general nonlinear, non-Gaussian case. The common feature of the three algorithms is the gain times error formula to implement the update step (to account for conditioning due to observations) in the filter. In contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF avoid the resampling of the particles in the importance sampling update step. Moreover, the gain times innovation feedback structure provides for error correction potentially leading to smaller simulation variance and improved stability properties. The paper also addresses the issue of non-uniqueness of the filter update formula and formulates a novel approximation algorithm based on ideas from optimal transport and coupling of measures. Performance of this and other algorithms is illustrated for a numerical example.
TOPICS: Filtration, Kalman filters, Filters, Algorithms, Particulate matter, Errors, Feedback, Stability, Simulation, Approximation, Particle filtering (numerical methods), Innovation
Tao Yang and Prashant Mehta
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037781
This paper is concerned with the problem of tracking single or multiple targets with multiple non-target specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization - to the nonlinear non-Gaussian case of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking applications.
TOPICS: Particulate matter, Feedback, Filters, Algorithms, Errors, Uncertainty, Filtration
Nurali Virani, Devesh K. Jha, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037782
This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.
TOPICS: Filtration, Dynamic models, Control algorithms, Computer programming, Density, Robots, Particle filtering (numerical methods), Simulation results
Nagavenkat Adurthi, Puneet Singla and Tarunraj Singh
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037783
This paper presents a computationally efficient approach to evaluate multidimensional expectation integrals. Specifically, certain non-product cubature points are constructed that exploit the symmetric structure of the Gaussian and uniform density functions. The proposed cubature points can be used as an efficient alternative to the Gauss-Hermite and Gauss-Legendre quadrature rules, but with significantly fewer number of points while maintaining the same order of accuracy when integrating polynomial functions in a multi-dimension space. The advantage of the newly developed points is made evident through few benchmark problems in nonlinear filtering and uncertainty propagation applications.
TOPICS: Density, Filtration, Dimensions, Polynomials, Uncertainty
Xue Iuan Wong and Manoranjan Majji
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037784
Image feature based localization and mapping applications useful in field-robotics are considered in this paper. Exploiting the continuity of image features and building upon the tracking algorithms that use point correspondences to provide an instantaneous localization solution, an extended Kalman filtering approach is formulated for estimation of the rigid body motion of the camera coordinates with respect to the world coordinate system. Recent results by the authors in quantifying uncertainties associated with the feature tracking methods form the basis for deriving scene dependent measurement error statistics that drive the optimal estimation approach. It is shown that the use of certain relative motion models between a static scene and the moving target can be recast as a recursive linear least squares problem and admits an efficient solution to the relative motion estimation problem that is amenable to real time implementations on board mobile computing platforms. The utility of the estimation approaches developed in the paper is demonstrated using stereoscopic terrain mapping experiments carried out using mobile robots. The map uncertainties estimated by the filter are utilized to establish the registration of the local maps into the global coordinate system.
TOPICS: Filtration, Algorithms, Robotics, Errors, Filters, Kalman filters, Mobile computing, Mobile robots, Motion estimation, Uncertainty, Statistics as topic
Ahmad Alshorman and Yildirim Hurmuzlu
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037735
Researchers often use mechanisms that consist of massless rods and concentrated masses in order to capture the dynamics of robotic locomotors. A kinematic prototyping tool that captures all possible locomotion modes of a given kinematic mechanism can be very useful in conceiving and designing such systems. Previously, we proposed a family of mechanisms that consist of two types of primitive building units: a single mass with a built-in revolute joint and a massless connection rod. This family starts from a single bouncing mass and progressively evolves into more complex generations. In this paper, we present a prototyping tool that generates all possible locomotion cycles of particle based, linear mechanisms. A new skip impact concept is introduced to describe the relative motion of the moving masses and the masses on the ground. Also, the paper represents a graphical user interface (GUI) that facilitates data input and the visualization of the locomotion modes.
TOPICS: Particulate matter, Chain, Kinematics, Graphical user interfaces, Dynamics (Mechanics), Design, Robotics, Visualization, Cycles, Rods
Qi Lu, Beibei Ren, Siva Parameswaran and Qing-Chang Zhong
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037736
This paper addresses the problem of autonomous trajectory tracking control for a quadrotor in a GPS-denied environment using only onboard sensing. To achieve that goal, it requires accurate estimation of quadrotor states followed by proper control actions. For the position estimation in a GPS-denied environment, an open source high speed optical flow sensor PX4FLOW is adopted. As for the quadrotor control, there are several challenges due to its highly nonlinear system dynamics, such as underactuation, coupling, model uncertainties and external disturbances. To deal with those challenges, the cascaded inner-outer uncertainty and disturbance estimator (UDE)-based robust control scheme has been developed and applied to the attitude and position control of a quadrotor. Extensive real flight experiments, including attitude stabilization, hover, disturbance rejection, trajectory tracking and comparison with the PID controller are carried out to demonstrate the effectiveness of the developed UDE-based controllers.
TOPICS: Trajectories (Physics), Tracking control, Uncertainty, Control equipment, Flow sensors, Nonlinear systems, Robust control, Position control, Flight, Dynamics (Mechanics)
Arash Mohtat, Colin/R. Gallacher and Jozsef Kovecses
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037733
In many haptic applications, producing a sharp feeling of impact is crucial for high-fidelity force feedback rendering of virtual objects. Although suitable for rendering collision-rich haptic interactions, impulse-based methods are rarely used in a pure form. Instead, they are combined with penalty-based elements in different forms such as virtual couplings and hybridization. In this paper, we first propose the direct impulse-based paradigm for rendering haptic contacts using a new sampled-data interpretation of the impact problem. Then, we cast this interpretation into a systematic framework entitled the generalized contact controller. This enables us to implement different contact rendering methods as controllers and to improve them by appropriating a wide array of analysis and design tools developed in the control field. We specifically show how to apply position and velocity corrections to the purely impulse-based contact controller for enhancing its energy and sustained contact characteristics, and how to add an anti-windup compensator for meeting actuation limits. These propositions are validated via simulation and experiments, as well as via human perception studies. Results show the promising aspects of the proposed impulse-based methods for generating a sharper unfiltered feeling of rigid-body contacts even at low sampling rates.
TOPICS: Impulse (Physics), Haptics, Rendering, Control equipment, Simulation, Collisions (Physics), Design, Couplings, Force feedback
Myo Thant Sin Aung, Zhan Shi and Ryo Kikuuwe
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037732
This paper proposes a new sliding mode filter augmented by a linear low-pass filter for mitigating the effect of high frequency noise. It is based on the derivation of three new variants of Jin et al.'s (2012) parabolic sliding mode filter (J-PSMF) and investigation on their frequency-response characteristics. The new filter is developed by augmenting one of the variants of J-PSMF by a second order linear low-pass filter. It has better balance between the noise attenuation and signal preservation than both linear low-pass filters and J-PSMF. The effectiveness of the new filter is experimentally evaluated on a DC servo motor equipped with an optical encoder. This paper also shows the application of the proposed filter to a positioning system under PDD2 (proportional, derivative, and second derivative) control, which successfully realizes the noise attenuation and the non-overshooting response simultaneously.
TOPICS: Filters, Low-pass filters, Position control, Noise (Sound), Signals, Frequency response, Preservation, Servomotors
Xiaowen Yu, Thomas Baker, Yu Zhao and Masayoshi Tomizuka
J. Dyn. Sys., Meas., Control   doi: 10.1115/1.4037734
In the protective glass manufacturing industry for cell phones, placing glass pieces into the slots of the grinder requires sub-millimeter accuracy which only can be achieved by human workers, leading to a bottle neck in the production line. To address such issue, industrial robot equipped with vision sensors is proposed to support human workers. The high placing performance is achieved by a two step approach. In the first step, an Eye-to-Hand camera is installed to detect the glass piece and slot with robust vision, which can put the glass piece close to the slot and ensures a primary precision. In the second step, a closed-loop controller based on visual servo is adopted to guide the glass piece into the slot with dual Eye-in-Hand cameras. However, vision sensor suffers from a very low frame rate and slow image processing speed resulting in a very slow placing performance. In addition, the placing performance is substantially limited by the system parameter uncertainty. To compensate for these limitations, a dual-rate Unscented Kalman Filter (UKF) with dual-estimation is adopted for sensor data filtering and on-line parameter identification without requiring any linear parameterization of the model. Experimental results are presented to confirm the effectiveness of the proposed approach.
TOPICS: Glass, Servomechanisms, Sensors, Control equipment, Assembly lines, Filtration, Robots, Tool grinders, Image processing, Kalman filters, Manufacturing industry, Uncertainty

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In