D.S. Bernstein

Palo Alto University, Palo Alto, California, United States

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Publications (567)328.11 Total impact

  • Source
    Xin Zhou, Tulga Ersal, Jeffrey L Stein, Dennis S Bernstein
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    ABSTRACT: This paper introduces a method to estimate battery state of health (SoH) via health-relevant electrochemical features. Battery state of health estimation is a critical part of battery management because it allows for balancing the trade-off between maximizing performance and minimizing degradation. In this paper, a health-relevant electrochemical feature, the side reaction current density, is used as the indicator of battery SoH. An estimation algorithm is required due to the unavailability of the side reaction current density via noninvasive methods. In this paper, Retrospective-Cost Subsystem Identification (RCSI) is used to estimate the side reaction current density via identification of an unknown battery health subsystem that generates the side reaction current density. Simulation results are provided for constant current charge and discharge cycles with different C rates. A current profile for an electric vehicle (EV) going through Urban Dynamometer Driving Schedule (UDDS) cycles is also used as the excitation signal during estimation. The simulations show promising results in battery health dynamic identification and side reaction current density estimation with RCSI.
    ASME Dynamic Systems and Control Conference, San Antonio, TX; 10/2014
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    ABSTRACT: This paper proposes a differential inflation scheme and applies this technique to driver estimation for the Global Ionosphere–Thermosphere Model (GITM) using the Ensemble Adjustment Kalman Filter (EAKF), which is a part of the Data Assimilation Research Testbed (DART). Driver estimation using EAKF is first demonstrated on a linear example and then applied to GITM. The Challenging Minisatellite Payload (CHAMP) neutral mass density measurements are assimilated into a biased version of GITM, and the solar flux index, F10.7, is estimated. Although the estimate of F10.7 obtained using DART does not converge to the measured values, the converged values are shown to drive the GITM output close to CHAMP measurements. In order to prevent the ensemble spread from converging to zero, the state and driver estimates are inflated. In particular, the F10.7 estimate is inflated to have a constant variance. It is shown that EAKF with differential inflation reduces the model bias from 73% down to 7% along the CHAMP satellite path when compared to the biased GITM output obtained without using data assimilation. The Gravity Recovery and Climate Experiment (GRACE) density measurements are used to validate the data assimilation performance at locations different from measurement locations. It is shown that the bias at GRACE locations is decreased from 76% down to 52% as compared to not using data assimilation, showing that model estimation of the thermosphere is improved globally.
    Journal of Atmospheric and Solar-Terrestrial Physics 11/2013; · 1.42 Impact Factor
  • Avishai Weiss, Ilya Kolmanovsky, Dennis S. Bernstein, Amit Sanyal
    Journal of Guidance Control and Dynamics 09/2013; 36(5):1425-1439. · 1.27 Impact Factor
  • E.D. Sumer, D.S. Bernstein
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    ABSTRACT: In this paper we consider sampled-data adaptive control in the presence of aliasing, due to either the high-frequency free response of the plant, or the high-frequency content in the disturbances. In particular, we present a numerical investigation of retrospective cost adaptive control (RCAC) applied to sampled-data command-following and disturbance-rejection problems, and investigate the performance of RCAC in the presence of aliasing. It is shown that RCAC is able to stabilize the plant despite the high-frequency dynamics, unless the controllability of unstable modes is not lost due to sampling. However, the intersample command-following performance may be nonzero due to aliasing of disturbances.
    American Control Conference (ACC), 2013; 01/2013
  • B.J. Coffer, K. Aljanaideh, D.S. Bernstein
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    ABSTRACT: An amplitude multiplexing technique for identifying Hammerstein systems with static nonlinearities is presented in this paper. The input signal to the Hammerstein system is amplitude multiplexed, and the output of the Hammerstein nonlinearity is approximated by a continuous piecewise affine function. The Hammerstein nonlinearity is assumed to pass through the origin. Using this identification technique, we show that, in the presence of zero-mean, colored output noise, the estimates of the Hammerstein nonlinearity and the impulse response of the linear plant are asymptotically correct up to a scalar factor.
    American Control Conference (ACC), 2013; 01/2013
  • Source
    Xin Zhou, Tulga Ersal, Jeffrey L. Stein, Dennis S. Bernstein
    Dynamic Systems and Control Conference, Palo Alto, CA; 01/2013
  • K. Aljanaideh, B.J. Coffer, D.S. Bernstein
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    ABSTRACT: Motivated by the potential advantages of FIR model structures, the present paper considers the applicability of FIR models to closed-loop identification of open-loop-unstable plants. We show that FIR models can be used effectively for closed-loop identification of open-loop-unstable plants. The key insight in this regard is to realize that a noncausal FIR model can serve as a truncated Laurent expansion inside the annulus between the asymptotically stable pole of largest modulus and the unstable pole of smallest modulus. The key to identifying the noncausal plant model is to delay the measured output relative to the measured input. With this techniques, the identified FIR model is precisely a noncausal approximation of the unstable plant, that is, an approximation of the Laurent expansion of the plant inside the annulus of analyticity lying between the disk of stable poles and the punctured plane of unstable poles.
    American Control Conference (ACC), 2013; 01/2013
  • Jin Yan, D.S. Bernstein
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    ABSTRACT: We extend retrospective cost adaptive control (RCAC) with auxiliary nonlinearities to command following for uncertain Hammerstein systems with non-monotonic input nonlinearities. We assume that only one Markov parameter of the linear plant is known and that the non-monotonic input nonlinearity is uncertain. Auxiliary nonlinearities are used within RCAC to account for the non-monotonic input nonlinearity. The required modeling information for the input nonlinearity includes the intervals of monotonicity as well as values of the nonlinearity that determine overlapping segments of the range of the nonlinearity within each interval of monotonicity.
    American Control Conference (ACC), 2013; 01/2013
  • Jesse B. Hoagg, Dennis S. Bernstein
    10/2012; 35(6).
  • Matthew S. Holzel, Dennis S. Bernstein
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    ABSTRACT: We consider polynomial matrix representations of MIMO linear systems and their connection to Markov parameters. Specifically, we consider polynomial matrix models in an arbitrary operator ρ, and develop theory and numerical algorithms for transforming polynomial matrix models into Markov parameter models, and vice versa. We also provide numerical examples to illustrate the proposed algorithms.
    Linear Algebra and its Applications 08/2012; 437(3):783–808. · 0.97 Impact Factor
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    ABSTRACT: We apply the forward-integrating Riccati-based feedback controller, which has been developed in our previous work for stabilization of time-varying systems, to a maneuvering spacecraft in an elliptic orbit around the Earth. We simulate rendezvous maneuvers on Molniya and Tundra orbits. We demonstrate that the controller performs well under thrust constraints, in the case where the spacecraft can thrust in only the orbital tangential direction, in the case where the thrusters may operate only intermittently due to faults or power availability, with thrust direction errors, and, finally, in an output feedback configuration where only relative position measurements are available.
    Decision and Control (CDC), 2012 IEEE 51st Annual Conference on; 01/2012
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    ABSTRACT: We extend retrospective cost adaptive control (RCAC) to command following for uncertain Hammerstein systems. We assume that only one Markov parameter of the linear plant is known and that the input nonlinearity is monotonic but otherwise unknown. Auxiliary nonlinearities are used within RCAC to account for the effect of the input nonlinearity.
    Decision and Control (CDC), 2012 IEEE 51st Annual Conference on; 01/2012
  • Source
    ASME Dynamic Systems and Control Conference, Ft. Lauderdale, FL; 01/2012
  • E.D. Sumer, D.S. Bernstein
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    ABSTRACT: We revisit the Rohrs counterexamples within the context of sampled-data adaptive control. In particular, retrospective cost adaptive control (RCAC) is applied to the sampled continuous-time plant with unmodeled high-frequency dynamics, which involves nonminimum-phase (NMP) sampling zeros. It is shown that, without knowledge of these NMP zeros, RCAC stabilizes the uncertain plant and asymptotically follows the sinusoidal command.
    Decision and Control (CDC), 2012 IEEE 51st Annual Conference on; 01/2012
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    ABSTRACT: Traditional system identification uses measurements of the inputs, but when these measurements are not available, alternative methods, such as blind identification, output-only identification, or operational modal analysis, must be used. Yet another method is sensor-to-sensor identification (S2SID), which estimates pseudo transfer functions whose inputs are outputs of the original system. A special case of S2SID is transmissibility identification. Since S2SID depends on cancellation of the input, this approach does not extend to nonlinear systems. However, in the present paper we show that, for the case of a two-output Hammerstein system, the least-squares estimate of the PTF is consistent, that is, asymptotically correct, despite the presence of the nonlinearities.
    Decision and Control (CDC), 2012 IEEE 51st Annual Conference on; 01/2012
  • E.D. Sumer, M.H. Holzel, A.M. D'Amato, D.S. Bernstein
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    ABSTRACT: In this paper we develop frequency-domain methods for approximating IIR plants with FIR transfer functions. The underlying goal is to increase the performance and robustness of Retrospective-Cost Adaptive Control (RCAC), which is applicable to MIMO possibly nonminimum-phase (NMP) plants without the need to know the locations of the NMP zeros. The only required modeling information is an FIR approximation of the plant, which may be based on a limited number of Markov parameters, or possibly noisy frequency response data. In this paper we investigate the resulting phase mismatch between the true plant and the FIR approximation obtained through linear and nonlinear approximation methods. We consider degradation in the phase mismatch due to uncertainty in the frequency response data.
    American Control Conference (ACC), 2012; 01/2012
  • A.M. D'Amato, D.S. Bernstein
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    ABSTRACT: Input reconstruction is a process where the inputs to a system are estimated using the measured system output, and possibly some modeling information from the system model. One way to achieve this goal is to invert the system model and cascade delays to guarantee that the inverse is proper. A standing issue in input reconstruction lies in the inversion of nonminimum-phase systems, since the inverse model is unstable. We consider two methods for achieving input reconstruction despite the presence of nonminimum-phase zeros. First, we develop an open-loop partial inversion of the system model using a finite number of frequency points, where the partial inverse is a finite impulse response model and therefore is guaranteed to be asymptotically stable. Second, we examine a closed-loop approach that uses an infinite impulse response model. We demonstrate both methods on several illustrative examples.
    American Control Conference (ACC), 2012; 01/2012
  • Source
    G. Marro, E. Zattoni, D. S. Bernstein
    18th IFAC World Congress; 08/2011
  • Source
    B.J. Coffer, J.B. Hoagg, D.S. Bernstein
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    ABSTRACT: We demonstrate that the retrospective cost adaptive control algorithm can improve the tracking error when following square-wave and triangle-wave commands in the presence of amplitude and rate saturation, respectively, provided that the saturation level is known. Specifically, the retrospective cost adaptive control algorithm does not experience integrator windup, which is a common problem under amplitude and rate saturation for fixed-gain controllers with integral action.
    American Control Conference (ACC), 2011; 08/2011
  • Source
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    ABSTRACT: Retrospective cost adaptive control (RCAC) can be applied to command following and disturbance rejection problems with plants that are possibly MIMO, unstable, and nonminimum phase. RCAC requires knowledge of a bound on the first nonzero Markov parameter as well as knowledge of the nonminimum-phase zeros of the plant, if any. The goal of the present paper is to increase the robustness of RCAC to uncertainty in the locations of the nonminimum-phase zeros. Specifically, a convex constraint is imposed on the poles of the controller in order to prevent the adaptive controller from attempting to cancel the nonminimum-phase zeros. Numerical results show that, when constrained convex optimization is used at each step, the transient response is improved and the adaptive controller has increased robustness to uncertainty in the locations of the nonminimum-phase zeros.
    American Control Conference (ACC), 2011; 08/2011

Publication Stats

6k Citations
328.11 Total Impact Points

Institutions

  • 2013
    • Palo Alto University
      Palo Alto, California, United States
  • 1992–2013
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
    • Arizona State University
      • Department of Mechanical Engineering
      Tempe, AZ, United States
  • 1992–2012
    • University of Michigan
      • Department of Aerospace Engineering
      Ann Arbor, MI, United States
  • 2011
    • University of Kentucky
      • Department of Mechanical Engineering
      Lexington, KY, United States
  • 2008–2010
    • Federal University of Minas Gerais
      • Departamento de Engenharia Eletrônica (DELT)
      Cidade de Minas, Minas Gerais, Brazil
    • Syracuse University
      • Department of Mechanical and Aerospace Engineering
      Syracuse, NY, United States
  • 1998–2010
    • Institute of Chemical Technology, Mumbai
      Mumbai, Mahārāshtra, India
  • 2009
    • University of Hawaiʻi at Mānoa
      • Department of Mechanical Engineering
      Honolulu, HI, United States
    • Palo Alto Research Center
      Palo Alto, California, United States
    • Indian Institute of Technology Madras
      Chennai, Tamil Nādu, India
  • 1998–2007
    • Air Force Research Laboratory
      Washington, Washington, D.C., United States
  • 2006
    • National University of Science and Technology
      Islāmābād, Islāmābād, Pakistan
    • KU Leuven
      • Department of Electrical Engineering (ESAT)
      Leuven, VLG, Belgium
  • 2003
    • Vehicle Control Technologies, Inc.
      Reston, Virginia, United States
  • 2000
    • Honeywell
      Morristown, New Jersey, United States
  • 1994–2000
    • Georgia Institute of Technology
      • School of Aerospace Engineering
      Atlanta, GA, United States
  • 1997
    • Purdue University
      • School of Aeronautics and Astronautics
      West Lafayette, Indiana, United States
  • 1995
    • Butler University
      Indianapolis, Indiana, United States
  • 1993–1994
    • Virginia Polytechnic Institute and State University
      • Department of Computer Science
      Blacksburg, VA, United States
  • 1987–1994
    • Florida Institute of Technology
      • Department of Mechanical and Aerospace Engineering
      Melbourne, FL, United States
  • 1989–1993
    • Massachusetts Institute of Technology
      • Department of Aeronautics and Astronautics
      Cambridge, Massachusetts, United States
  • 1984–1992
    • Harris Corporation
      Melbourne, Florida, United States