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1. Stable nullspace adaptive parameter identification of 6 degree-of-freedom plant and actuator models for underactuated vehicles: Theory and experimental evaluation

   https://doi.org/10.1177/02783649231191184  

   Harris, Zachary J.; Mao, Annie M.; Paine, Tyler M.; Whitcomb, Louis L. (November 2023, The International Journal of Robotics Research)

   Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. 

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2. Estimation of Parameter Probability Distributions for Lithium-Ion Battery String Models Using Bayesian Methods

   https://doi.org/10.1115/DSCC2020-3218  

   Couto, Luis D.; Zhang, Dong; Aitio, Antti; Moura, Scott; Howey, David (October 2020, ASME 2020 Dynamic Systems and Control Conference)

   Abstract This paper addresses the parameter estimation problem for lithium-ion battery pack models comprising cells in series. This valuable information can be exploited in fault diagnostics to estimate the number of cells that are exhibiting abnormal behaviour, e.g. large resistances or small capacities. In particular, we use a Bayesian approach to estimate the parameters of a two-cell arrangement modelled using equivalent circuits. Although our modeling framework has been extensively reported in the literature, its structural identifiability properties have not been reported yet to the best of the authors’ knowledge. Moreover, most contributions in the literature tackle the estimation problem through point-wise estimates assuming Gaussian noise using e.g. least-squares methods (maximum likelihood estimation) or Kalman filters (maximum a posteriori estimation). In contrast, we apply methods that are suitable for nonlinear and non-Gaussian estimation problems and estimate the full posterior probability distribution of the parameters. We study how the model structure, available measurements and prior knowledge of the model parameters impact the underlying posterior probability distribution that is recovered for the parameters. For two cells in series, a bimodal distribution is obtained whose modes are centered around the real values of the parameters for each cell. Therefore, bounds on the model parameters for a battery pack can be derived. 

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3. On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

   https://doi.org/10.1149/1945-7111/acbc9c  

   Fogelquist, Jackson; Lai, Qingzhi; Lin, Xinfan (March 2023, Journal of The Electrochemical Society)

   Emerging lithium-ion battery systems require high-fidelity electrochemical models for advanced control, diagnostics, and design. Accordingly, battery parameter estimation is an active research domain where novel algorithms are being developed to calibrate complex models from input-output data. Amidst these efforts, little focus has been placed on the fundamental mechanisms governing estimation accuracy, spurring the question, why is an estimate accurate or inaccurate? In response, we derive a generalized estimation error equation under the commonly adopted least-squares objective function, which reveals that the error can be represented as a combination of system uncertainties (i.e., in model, measurement, and parameter) and uncertainty-propagating sensitivity structures in the data. We then relate the error equation to conventional error analysis criteria, such as the Fisher information matrix, Cramér-Rao bound, and parameter sensitivity, to assess the benefits and limitations of each. The error equation is validated through several uni- and bivariate estimations of lithium-ion battery electrochemical parameters using experimental data. These results are also analyzed with the error equation to study the error compositions and parameter identifiability under different data. Finally, we show that adding target parameters to the estimation without increasing the amount of data intrinsically reduces the robustness of the results to system uncertainties. 

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4. Guidance on evaluating parametric model uncertainty at decision-relevant scales

   https://doi.org/10.5194/hess-26-2519-2022  

   Smith, Jared D.; Lin, Laurence; Quinn, Julianne D.; Band, Lawrence E. (January 2022, Hydrology and Earth System Sciences)

   Abstract. Spatially distributed hydrological models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing the engineering decision objectives. Identifying parameters that describe physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using multipliers to adjust the values of spatially distributed parameters to further reduce dimensionality. We evaluate six sensitivity metrics, four of which align with decision objectives and two of which consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results for four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that (1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, (2) evaluating sensitivity metrics at the basin outlet misses many parameters that control streamflows in hillslopes, and (3) for some multipliers, calibrating all parameters in the set being adjusted may be preferable to using the multiplier if parameter sensitivities are significantly different, while for others, calibrating a subset of the parameters may be preferable if they are not all influential. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it. 

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5. Mathematical Modelling of Reactive Inks for Additive Manufacturing of Charged Membranes

   Liu, X.; De, R.; Perez, A.; Hoffman, J.R.; Phillip, W.A.; Dowling, A.W. (July 2022, Computer aided chemical engineering)

   Yamashita, Y.; Kano, M. (Ed.)

   Patterned charged membranes with engendered useful characteristics can offer selective transport of electrolytes. Chemical patterning across the membrane surface via a physical inkjet deposition process requires precise control of the reactive-ink formulation, which enables the introduction of charged functionality to the membrane. This study develops a new dynamic mathematical model for the primary step of the batch reactive-ink formulation considering an ink mixture of copper sulphate and ascorbic acid. Nonlinear least squares parameter estimation is performed to infer three kinetic model parameters by analysing data from nine dynamic experiments simultaneously. Global sensitivity and Fisher information matrix (FIM) analyses reveal only one kinetic parameter is identifiable from time-series pH measurements. The fitted model can capture the overall nonlinear dynamics of the batch reaction and works best for initial Cu2 + concentrations between 30 and 50 mM. Time-series Cu2 + or Cu+ concentration measurements are recommended in future experiments to elucidate the kinetics of reactive-ink formulation. 

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