Control for Safety Specifications of Systems With Imperfect Information on a Partial Order

In this paper, we consider the control problem for uncertain systems with imperfect information, in which an output of interest must be kept outside an undesired region (the bad set) in the output space. The state, input, output, and disturbance spaces are equipped with partial orders. The system dynamics are either input/output order preserving with output in R[superscript 2] or given by the parallel composition of input/output order preserving dynamics each with scalar output. We provide necessary and sufficient conditions under which an initial set of possible system states is safe, that is, the corresponding outputs are steerable away from the bad set with open loop controls. A closed loop control strategy is explicitly constructed, which guarantees that the current set of possible system states, as obtained from an estimator, generates outputs that never enter the bad set. The complexity of algorithms that check safety of an initial set of states and implement the control map is quadratic with the dimension of the state space. The algorithms are illustrated on two application examples: a ship maneuver to avoid an obstacle and safe navigation of an helicopter among buildings.National Science Foundation (U.S.) (CAREER Award CNS-0642719

Safety control of piece-wise continuous order preserving systems

This paper is concerned with safety control of systems with imperfect state information and disturbance input. Specifically, we consider the class of systems whose dynamics preserve a partial ordering. We provide necessary and sufficient conditions under which a given set of initial states is steerable away from a specified bad set. Moreover, a control strategy is provided that guarantees that the bad set is avoided. Such characterization is achieved for order preserving systems while for general systems only an approximated solution is achievable. A method for implementation of the control strategy is provided and the effectiveness of the proposed method is illustrated via a numerical example and employed for obstacle avoidance of a ship.National Science Foundation (U.S.) (NSF CAREER AWARD # CNS-0642719

Kinetics of Hepatitis B Virus Infection: A Cellular Automaton Model Study

We created a simple cellular automata (CA) model for hepatitis B infection dynamics associated with spatial structure performed under various ages of liver tissue correspond to different immune responses in order to study the effect of spatial heterogeneities on the dynamical evolution of a viral infection. The results of the simulations show biphasic nature of viral load decreases, as observed in the acute infection in real state. Our results also confirm the importance of the hepatocyte target cells, the spatial localization, and the local interactions on the dynamics of HBV infection, whereas models based on ordinary differential equations are not considered. Our model is quite simple with four states and only five parameters, however, the dynamics from the model qualitatively equivalent clinical data.

Improving power theft detection using efficient clustering and ensemble classification

One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks

Robust Model Based Control of Constrained Systems.

This dissertation is concerned with control of systems subject to input and state constraints. Model Predictive Control (MPC) is one promising control technique that is capable of dealing with constraints. Its flexible formulation also provides mechanisms to tune the closed loop system for desired performance. However, due to computational complexity and its dependency on accurate models of the system, the MPC applications for systems with fast dynamics or with model uncertainties are not wide spread. The focus of this dissertation is to develop methodologies and tools that can enhance the computational efficiency and address robustness issues of constrained dynamic systems. The core contribution of this dissertation is that it provides a computational efficient MPC solver, referred to as InPA-SQP (Integrated Perturbation Analysis and Sequential Quadratic Programming). The main results include four major components. First, a neighboring extremal control method is proposed for discrete-time optimal control problems subject to a general class of inequality constraints. A closed form solution for the neighboring extremal (NE) control is provided and a sufficient condition for existence of the neighboring extremal solution is specified. Second, the NE method is integrated with sequential quadratic programming that leads to InPA-SQP. Third, a robust control method is introduced for linear discrete-time systems subject to mixed input-state constraints. Unlike conventional MPC, the method does not require repeatedly solving an optimization problem online while guarantees states convergence to a minimal invariant set. Fourth, it is shown that if the dynamics of disturbances are incorporated, the attractor set associated with the proposed constrained robust control methods can be considerably smaller, leading to a much less conservative design. Applications of the InPA-SQP and proposed constrained robust control constitute the other key element of the study. The InPA-SQP is employed in two experimental applications: one for voltage regulation of a DC/DC converter and another for path following of a model ship. Both applications show effectiveness of the method in terms of computation and constraints handling. These applications not only serve as validation platforms but also motivate new research topics for further investigation.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77854/1/ghaemi_1.pd

A method for determining the robustness of bio-molecular oscillator models

Abstract Background Quantifying the robustness of biochemical models is important both for determining the validity of a natural system model and for designing reliable and robust synthetic biochemical networks. Several tools have been proposed in the literature. Unfortunately, multiparameter robustness analysis suffers from computational limitations. Results A novel method for quantifying the robustness of oscillatory behavior to parameter perturbations is presented in this paper. This method relies on the combination of Hopf bifurcation and Routh-Hurwitz stability criterion, which is widely applied in control system design. The proposed method is employed to calculate the robustness of two oscillating biochemical network models previously analyzed in the literature. The robustness bounds here obtained are tighter than what was previously obtained in the literature for both models. Conclusion The method here proposed for quantifying the robustness of biochemical oscillator models is computationally less demanding than similar multiparamter variation techniques available in the literature. It also provides tighter bounds on two models previously analyzed in the literature.http://deepblue.lib.umich.edu/bitstream/2027.42/112631/1/12918_2009_Article_363.pd

A review: accuracy optimization in clustering ensembles using genetic algorithms

The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research

A method for determining the robustness of bio-molecular oscillator models

BACKGROUND: Quantifying the robustness of biochemical models is important both for determining the validity of a natural system model and for designing reliable and robust synthetic biochemical networks. Several tools have been proposed in the literature. Unfortunately, multiparameter robustness analysis suffers from computational limitations. RESULTS: A novel method for quantifying the robustness of oscillatory behavior to parameter perturbations is presented in this paper. This method relies on the combination of Hopf bifurcation and Routh-Hurwitz stability criterion, which is widely applied in control system design. The proposed method is employed to calculate the robustness of two oscillating biochemical network models previously analyzed in the literature. The robustness bounds here obtained are tighter than what was previously obtained in the literature for both models. CONCLUSION: The method here proposed for quantifying the robustness of biochemical oscillator models is computationally less demanding than similar multiparamter variation techniques available in the literature. It also provides tighter bounds on two models previously analyzed in the literature

The role of hepatitis B virus genome variations in HBV-related HCC: effects on host signaling pathways

Hepatocellular carcinoma (HCC) is a significant global health issue, with a high prevalence in many regions. There are variations in the etiology of HCC in different regions, but most cases are due to long-term infection with viral hepatitis. Hepatitis B virus (HBV) is responsible for more than 50% of virus-related HCC, which highlights the importance of HBV in pathogenesis of the disease. The development and progression of HBV-related HCC is a complex multistep process that can involve host, viral, and environmental factors. Several studies have suggested that some HBV genome mutations as well as HBV proteins can dysregulate cell signaling pathways involved in the development of HCC. Furthermore, it seems that the pathogenicity, progression of liver diseases, response to treatment and also viral replication are different among HBV mutants. Understanding the relationship between HBV genome variations and host signaling pathway alteration will improve our understanding of the molecular pathogenesis of HBV-related HCC. Furthermore, investigating commonly dysregulated pathways in HBV-related HCC is necessary to discover more specific therapeutic targets and develop more effective strategies for HCC treatment. The objective of this review is to address the role of HBV in the HCC progression and primarily focus on the impacts of HBV genome variations on HCC-related signaling pathways