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 reconstruction of both unknown inputs and unknown initial conditions, and thus is a stronger notion than l-delay left invertibility, which allows(More)
— The purpose of this work is to compare model structures and identification algorithms for estimating Markov parameters in the presence of uncorrelated and correlated input, process, and output noise. We consider several least-squares variants with ARX and µ-Markov model structures, which are compared with white noise identification signals.
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)
— In this paper, we introduce the concept of input and state observability, that is, conditions under which both the unknown input and state can be estimated from the output measurements. We discuss sufficient and necessary conditions for a discrete-time system to be input and state observable. Next, we derive an unbiased minimum-variance filter to estimate(More)
— First principle models and empirical models are necessarily approximate. In this paper we develop two empirical approaches that use a delta model to modify an initial model by means of cascade, parallel or feedback augmentation. A sub-space based nonlinear identification algorithm and an adaptive disturbance rejection algorithm are both used to construct(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)
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)
— For nonlinear systems with measured-input non-linearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selective-refinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the(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(More)