Adaptive stabilization of a class of nonlinear systems using high-gain feedback

This note presents a high-gain feedback stabilizing control algorithm in which the high-gain parameter is adapted on-line. The algorithm is developed for a class of nonlinear systems which can be viewed as the nonlinear counterpart of uniform rank systems. The system can be unknown except for a number of vital pieces of information. For single-input single-output linear systems such information is usually required in the traditional adaptive control literature.     

Application of a Compaction Simulator to the Design of a High-Dose Tablet Formulation. Part I

The compaction properties of an investigational drug are studied by the use of a compaction simulator. The effects of punch velocity over the range of 30-640 mm^-1 on the compaction properties of the pure drug and a variety of formulas incorporating a high dose of the active compound have been investigated. The data were analyzed by applying the Heckel equation. The pure drug was found to have a high yield pressure at a relatively low punch velocity of 31 mm^-1. As the punch velocity was increased there was a decrease in crushing strength, primarily as a result of increasing yield pressure. These findings indicate that the pure drug predominantly consolidated by fragmentation and elastic deformation, with a slow plastically deforming component. The information obtained on the consolidation mechanism of the pure drug and, subsequently, on model formulas were instrumental in the design and selection of a robust formula and granulation process. The advantages of conducting dosage form design and characterization studies during the early phase of tablet formulation using means such as a compaction simulator are emphasized in this investigation.     

Squaring down of general MIMO systems to invertible uniform rank systems via pre- and/or post-compensators

Pre- and/or post-compensators are designed to square down a general MIMO system to a uniform rank system, the structure of which is almost similar to a SISO system. The method of squaring down either does not change the finite zero structure of the given MIMO system or simply adds additional finite zeros in the left half s-plane. The significance of such a squaring down lies in the simplicity of the structure of a uniform rank system lending itself for easy analysis and control design.     

Synchronization in heterogeneous networks of discrete‐time introspective right‐invertible agents

This paper studies output synchronization problem, formation problem, and regulated synchronization problem for a heterogenous network of discrete‐time introspective right‐invertible agents. We first propose a decentralized control scheme to solve the output synchronization problem for a set of communication topologies. Moreover, if the synchronization trajectories are assumed to be bounded, a universal controller can be constructed for all communication topologies, which contain a directed spanning tree. The design can be applied to solve the formation problem with arbitrary formation vectors. In the regulated synchronization problem, we assume only the root receives information from exosystem. We then design a decentralized controller to solve the problem for a set of communication topologies. Copyright © 2013 John Wiley & Sons, Ltd.     

Comments on the use of buckminsterfullerene encapsulated in zeolite Y as a potential catalyst

Buckminsterfullerene encapsulated within zeolite Y is found not to be a reactive radical initiator for the formation of dimethoxyethane from dimethyl ether below 200° C. Above this temperature the expected acid catalysed conversion of dimethyl ether to hydrocarbons is observed. Dimethoxyethane formation is observed when bibenzoyl peroxide is used as a radical initiator. These studies indicate that any radical formed by the encapsulated buckminsterfullerene is relatively unreactive.     

Dynamic behavior of the discrete‐time double integrator with saturated locally stabilizing linear state feedback laws

The main contribution of this paper is to completely characterize the dynamic behavior of the discrete‐time double integrator with a saturated locally stabilizing linear state feedback law. In continuous‐time setting, any linear state feedback control law that locally stabilizes the double integrator also globally stabilizes the system in the presence of actuator saturation. In discrete‐time setting, the equivalent of the double integrator does not have the same property. In this paper, we completely characterize the global behavior of saturated locally stabilizing linear state feedback laws for the discrete‐time double integrator. Copyright © 2012 John Wiley & Sons, Ltd.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.