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- Moving horizon estimation (3)
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Articles 1 - 7 of 7
Full-Text Articles in Engineering
A Direct Sampling Particle Filter From Approximate Conditional Density Function Supported On Constrained State Space, Sridhar Ungarala
A Direct Sampling Particle Filter From Approximate Conditional Density Function Supported On Constrained State Space, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Constraints on the state vector must be taken into account in the state estimation problem. Recently, acceptance/rejection and projection methods are proposed in the particle filter framework for constraining the particles. A weighted least squares formulation is used for constraining samples in unscented and ensemble Kalman filters. In this paper, direct sampling from an approximate conditional probability density function (pdf) is proposed. It is obtained by approximating the a priori pdf as a Gaussian. The support of the conditional density is a subset of the intersection of two supports, the 3-sigma bounds of the priori Gaussian and the constrained state …
Computing Arrival Cost Parameters In Moving Horizon Estimation Using Sampling Based Filters, Sridhar Ungarala
Computing Arrival Cost Parameters In Moving Horizon Estimation Using Sampling Based Filters, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Moving horizon estimation (MHE) is a numerical optimization based approach to state estimation, where the joint probability density function (pdf) of a finite state trajectory is sought, which is conditioned on a moving horizon of measurements. The joint conditional pdf depends on the a priori state pdf at the start of the horizon, which is a prediction pdf based on historical data outside the horizon. When the joint pdf is maximized, the arrival cost is a penalty term based on the a priori pdf in the MHE objective function. Traditionally, the a priori pdf is assumed as …
Letter To The Editor, Sridhar Ungarala
Letter To The Editor, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
No abstract provided.
Comments On "Robust And Reliable Estimation Via Unscented Recursive Nonlinear Dynamic Data Reconciliation", Sridhar Ungarala
Comments On "Robust And Reliable Estimation Via Unscented Recursive Nonlinear Dynamic Data Reconciliation", Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
No abstract provided.
The Use Of A Cell Filter For State Estimation In Closed-Loop Nmpc Of Low Dimensional Systems, Sridhar Ungarala, Keyu Li
The Use Of A Cell Filter For State Estimation In Closed-Loop Nmpc Of Low Dimensional Systems, Sridhar Ungarala, Keyu Li
Chemical & Biomedical Engineering Faculty Publications
Combining variants of the Kalman filter and moving horizon estimation (MHE) with nonlinear MPC has been studied before. The MHE is appealing due to its ability to impose constraints and demonstrated superiority over extended Kalman filter. However, nonlinear MPC based on MHE requires solutions to two back to back nonlinear programs. In this paper we propose to use the cell filter (CF) to provide state feedback to the MPC regulator. The cell filter is a piecewise constant approximation of the conditional probability density of the states, whose temporal evolution is modeled by an aggregate Markov chain. Since the CF …
Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems, Lixin Lang, Wen-Shiang Chen, Bhavik R. Bakshi, Prem K. Goel, Sridhar Ungarala
Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems, Lixin Lang, Wen-Shiang Chen, Bhavik R. Bakshi, Prem K. Goel, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Nonlinear and non-Gaussian processes with constraints are commonly encountered in dynamic estimation problems. Methods for solving such problems either ignore the constraints or rely on crude approximations of the model or probability distributions. Such approximations may reduce the accuracy of the estimates since they often fail to capture the variety of probability distributions encountered in constrained linear and nonlinear dynamic systems. This article describes a practical approach that overcomes these shortcomings via a novel extension of sequential Monte Carlo (SMC) sampling or particle filtering. Inequality constraints are imposed by accept/reject steps in the algorithm. The proposed approach provides samples representing …
Time-Varying System Identification Using Modulating Functions And Spline Models With Application To Bio-Processes, Sridhar Ungarala, Tomas B. Co
Time-Varying System Identification Using Modulating Functions And Spline Models With Application To Bio-Processes, Sridhar Ungarala, Tomas B. Co
Chemical & Biomedical Engineering Faculty Publications
Time dependent parameters are frequently encountered in many real processes which need to be monitored for process modeling, control and supervision purposes. Modulating functions methods are especially suitable for this task because they use the original continuous-time differential equations and avoid differentiation of noisy signals. Among the many versions of the method available, Pearson–Lee method offers a computationally efficient alternative. In this paper, Pearson–Lee method is generalized for non-stationary continuous-time systems and the on-line version is developed. The time dependent parameters are modeled as polynomial splines inside a moving data window and recursion formulae using shifting properties of …