Asad A. Ali

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Health management of Li-ion batteries depends on knowledge of certain battery internal dynamics (e.g., lithium consumption and film growth at the solid-electrolyte interface) whose inputs and outputs are not directly measurable with noninva-sive methods. This presents a problem of identification of inaccessible subsystems. To address this problem, we apply(More)
We develop a method for obtaining state estimates for a possibly nonminimum-phase system in the presence of an unknown harmonic input. We construct a state estimator based on the system model, and then introduce an estimator input provided by an adap-tive feedback model whose goal is to drive the estimated output to the measured output despite the presence(More)
Retrospective cost optimization was originally developed for adaptive control. In this paper, we show how this technique is applicable to three distinct but related problems, namely, state estimation, input estimation, and model refinement. To illustrate these techniques, we give two examples. In the first example, retrospective cost model refinement is(More)
We consider the problem of estimating the unknown solar driver F10.7 and physical states in the ionosphere and thermosphere using retrospective cost adaptive state estimation (RCASE). We interface RCASE with the Global Ionosphere Thermosphere Model (GITM) to demonstrate state estimation and F10.7 input reconstruction. We further examine the various factors(More)
— We develop a method for identifying SISO Ham-merstein systems with an unknown static nonlinearity, linear dynamics, white input noise and colored output noise. We use least squares with a µ-Markov model to estimate the Markov parameters of the linear time-invariant dynamical system. Since the input to the linear system is not available, we use a(More)
— We consider the problem of data-based model refinement, where we assume the availability of an initial model, which may incorporate both physical laws and empirical observations. With this initial model as a starting point, our goal is to use additional measurements to refine the model. In particular, components of the model that are poorly modeled can be(More)
— In this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters.(More)
— We present a growing-window variable-regularization recursive least squares (GW-VR-RLS) algorithm. Standard recursive least squares (RLS) uses a time-invariant regularization. More specifically, the inverse of the initial covariance matrix in classical RLS can be viewed as a regularization term, which weights the difference between the next state estimate(More)
Dramatic improvements in the rise-and fall-time characteristics of power MOSFETs enable PWM switching frequencies to increase by an order of magnitude. The resulting drop in required inductance permits co-packaging of the inductor and the power-supply controller. Fig. 1. Simplified schematic of a typical synchronous buck converter. The inductor current(More)
— We consider the notion of persistency within a deterministic, finite-data context, namely, in terms of the rank and condition number of the regressor matrix, which contains input and output data. The novel contribution of this work is the technique of zero buffering, in which the input signal begins with a sequence of zeros. We show that the degree of(More)