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- Bruno Otávio Soares Teixeira, Jaganath Chandrasekar, Harish J. Palanthandalam-Madapusi, Leonardo A. B. Tôrres, Luis Antonio Aguirre, Dennis S. Bernstein
- IEEE Transactions on Signal Processing
- 2008

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)

Prior results on input reconstruction for multi-input, multi-output discretetime linear systems are extended by defining l-delay input and initial-state observability. 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 input… (More)

Systems with unknown inputs have received considerable attention [4–23, 25, 26, 28–30]. The unknown inputs may represent unknown external drivers, input uncertainty, or instrument faults. An active research area is state reconstruction with known model equations and unknown inputs. Approaches include full-order observers [5, 7, 10, 16, 17, 30],… (More)

- Harish J. Palanthandalam-Madapusi, Tobin H. Van Pelt, Dennis S. Bernstein
- Int. J. Control
- 2010

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)

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 model… (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)

Abstract—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 extend previous results on input and state observability for discrete-time systems by considering a more general framework. Particularly, we… (More)

- Gaurav Kumar Singh, Vrutangkumar V. Shah, Harish J. Palanthandalam-Madapusi
- 2013 IEEE International Conference on Control…
- 2013

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)

Long length-scale structural deformations of DNA play a central role in many biological processes including gene expression. While all-atom based Molecular Dynamics (MD) tools fall short to simulate the long-length and time scale mechanics of DNA, continuum rod model of DNA has emerged as a viable tool. The continuum rod model predictions are however very… (More)