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— Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to undesired variability of system outputs and, therefore, a notable degradation of closed-loop performance. This paper investigates model predictive control of nonlinear dynamical systems subject to probabilistic parametric uncertainties. A nonlinear model predictive(More)
This paper presents a comparative analysis of various nonlinear estimation techniques when applied for output feedback model-based control of batch crystallization processes. Several nonlinear observers, namely an extended Luenberger observer, an extended Kalman filter, an unscented Kalman filter, an ensemble Kalman filer and a moving horizon estimator are(More)
Stringent requirements on safety and availability of high-performance systems necessitate reliable fault detection and isolation in the event of system failures. This paper investigates active fault diagnosis of nonlinear systems with probabilistic, time-invariant uncertainties of the parameters and initial conditions. A probabilistic model-based approach(More)
This article presents an output feedback nonlinear model-based control approach for optimal operation of industrial batch crystallizers. A full population balance model is utilized as the cornerstone of the control approach. The modeling framework allows us to describe the dynamics of a wide range of industrial batch crystallizers. In addition, it(More)
— Probabilistic uncertainties and constraints are ubiquitous in complex dynamical systems and can lead to severe closed-loop performance degradation. This paper presents a fast algorithm for stochastic model predictive control (SMPC) of high-dimensional stable linear systems with time-invariant probabilistic uncertainties in initial conditions and system(More)
Accurate estimation of parameters is paramount in developing high-fidelity models for complex dynamical systems. Model-based optimal experiment design (OED) approaches enable systematic design of dynamic experiments to generate input-output data sets with high information content for parameter estimation. Standard OED approaches however face two challenges:(More)
An on-line optimization strategy is developed and applied to a semi-industrial crystallization process. The seeded fed-batch crystallizer is represented by a nonlinear moment model. An optimal control problem pertinent to maximization of the batch crystal yield is solved using the sequential optimization approach. As the dynamic optimizer requires knowledge(More)
— The time-varying dynamics of real systems often limit the lifetime performance of model predictive control applications. A critical step for cost-effective maintenance of control systems is to distinguish between control-relevant plant changes and variations in disturbance characteristics in the event of an observed closed-loop performance drop. This(More)
Building dynamic models is important in many applications including model-based design, optimization, and control. When multiple hypothesized models have predictions that are consistent with the measurements, experimental design is used to discriminate between the models. This task is particularly challenging for nonlinear systems subject to uncertainties.(More)
This article presents a model-based control approach for optimal operation of a seeded fed-batch evaporative crystallizer. Various direct optimization strategies, namely, single shooting, multiple shooting, and simultaneous strategies, are used to examine real-time implementation of the control approach on a semi-industrial crystal-lizer. The dynamic(More)