Fault diagnosis and fault-tolerant control for nonlinear systems with linear output structure

Article describes the process of fault diagnosis and fault-tolerant control for nonlinear systems with linear output structure

Shaping of molecular weight distribution using b-spline based predictive probability density function control

Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms

Shaping of molecular weight distribution by iterative learning probability density function control strategies

A mathematical model is developed for the molecular weight distribution (MWD) of free-radical styrene polymerization in a simulated semi-batch reactor system. The generation function technique and moment method are employed to establish the MWD model in the form of Schultz-Zimmdistribution. Both static and dynamic models are described in detail. In order to achieve the closed-loop MWD shaping by output probability density function (PDF) control, the dynamic MWD model is further developed by a linear B-spline approximation. Based on the general form of the B-spline MWD model, iterative learning PDF control strategies have been investigated in order to improve the MWD control performance. Discussions on the simulation studies show the advantages and limitations of the methodology

Direct identification of continuous second - order plus dead-time model

Describes a direct identification of continuous second - order plus dead-time model

Sensitivity analysis and experimental design of a stiff signal transduction pathway model

Sensitivity analysis is normally used to analyze how sensitive a system is with respect to the change of parameters or initial conditions and is perhaps best known in systems biology via the formalism of metabolic control analysis [1, 2]. The nuclear factor B (NF-B) signalling pathway is an important cellular signalling pathway, of which protein phosphorylation is a major factor controlling the activation of further downstream events. The NF-κB proteins regulate numerous genes that play important roles in inter- and intra-cellular signalling, cellular stress responses, cell growth, survival, and apoptosis. As such, its specificity and its role in the temporal control of gene expression are of crucial physiological interest

Modeling near-field tsunami observations to improve finite-fault slip models for the 11 March 2011 Tohoku earthquake

The massive tsunami generated by the 11 March 2011 Tohoku earthquake (M_w 9.0) was widely recorded by GPS buoys, wave gauges, and ocean bottom pressure sensors around the source. Numerous inversions for finite-fault slip time histories have been performed using seismic and/or geodetic observations, yielding generally consistent patterns of large co-seismic slip offshore near the hypocenter and/or up-dip near the trench, where estimated peak slip is ~60 m. Modeling the tsunami generation and near-field wave processes using two detailed rupture models obtained from either teleseismic P waves or high-rate GPS recordings in Japan allows evaluation of how well the finite-fault models account for the regional tsunami data. By determining sensitivity of the tsunami calculations to rupture model features, we determine model modifications that improve the fit to the diverse tsunami data while retaining the fit to the seismic and geodetic observations

Minimum Sparsity of Unobservable Power Network Attacks

Physical security of power networks under power injection attacks that alter generation and loads is studied. The system operator employs Phasor Measurement Units (PMUs) for detecting such attacks, while attackers devise attacks that are unobservable by such PMU networks. It is shown that, given the PMU locations, the solution to finding the sparsest unobservable attacks has a simple form with probability one, namely, $\kappa(G^M) + 1$, where $\kappa(G^M)$ is defined as the vulnerable vertex connectivity of an augmented graph. The constructive proof allows one to find the entire set of the sparsest unobservable attacks in polynomial time. Furthermore, a notion of the potential impact of unobservable attacks is introduced. With optimized PMU deployment, the sparsest unobservable attacks and their potential impact as functions of the number of PMUs are evaluated numerically for the IEEE 30, 57, 118 and 300-bus systems and the Polish 2383, 2737 and 3012-bus systems. It is observed that, as more PMUs are added, the maximum potential impact among all the sparsest unobservable attacks drops quickly until it reaches the minimum sparsity.Comment: submitted to IEEE Transactions on Automatic Contro