Harish J. Palanthandalam-Madapusi

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Prior results on input reconstruction for multi-input, multi-output discrete-time linear systems are extended by defining l-delay input and initial-state observ-ability. This property provides the foundation for reconstructing both unknown inputs and unknown initial conditions, and thus is a stronger notion than l-delay left invertibility, which allows(More)
SUMMARY This paper considers the concept of input and state observability, that is, conditions under which both the unknown input and initial state of a known model can be determined from output measurements. We provide necessary and sufficient conditions for input and state observability in discrete-time systems. Next, we develop a subspace identification(More)
This paper considers the state-estimation problem with a constraint on the data-injection gain. Special cases of this problem include the enforcing of a linear equality constraint in the state vector, the enforcing of unbiased estimation for systems with unknown inputs, and simplification of the estimator structure for large-scale systems. Both the one-step(More)
— We compare several reduced-order Kalman filters for discrete-time LTI systems based on reduced-order error-covariance propagation. These filters use combinations of balanced model truncation and complementary steady-state covariance compensation. After describing each method, we compare their performance through numerical studies using a compartmental(More)
An approximate input reconstruction algorithm is used to reconstruct unknown inputs, which are then used for fault detection. The approximate input reconstruction algorithm is a least squares algorithm that estimates both the unknown initial state and input history. The estimated inputs are then compared to the commanded values and sensor values to assess(More)
In this article, we investigate the consistency of parameter estimates obtained from least-squares identification with a quadratic parameter constraint. For generality, we consider infinite impulse-response systems with coloured input and output noise. In the case of finite data, we show that there always exists a possibly indefinite quadratic constraint(More)
Parkinson's disease is characterized by increased reaction times in both voluntary and involuntary motor responses and often results in unintended oscillatory motion of body parts, termed as Parkinsonian tremor. A simple and efficient method for diagnosing Parkinson's disease is still not available and furthermore, on the correct diagnosis of Parkinson's(More)
— Solar storms can damage transformers, electrical networks, and satellites. In this paper, we use system identification methods to construct nonlinear time-series models that are used to predict solar wind conditions with a 27-day prediction horizon. To identify nonlinear time-series models, we use a set of basis functions to represent the nonlinear(More)