Chandra Kambhampati

Fault Tolerant Control in NCS Medium Access Constraints

This paper deals with the problem of fault-tolerant control of a Network Control System (NCS) for the case in which the sensors, actuators and controller are inter-connected via various Medium Access Control protocols which define the access scheduling and collision arbitration policies in the network and employing the so-called periodic communication sequence. A new procedure for controlling a system over a network using the concept of an NCS-Information-Packet is described which comprises an augmented vector consisting of control moves and fault flags. The size of this packet is used to define a Completely Fault Tolerant NCS. The fault-tolerant behaviour and control performance of this scheme is illustrated through the use of a process model and controller. The plant is controlled over a network using Model-based Predictive Control and implemented via MATLABcopy and LABVIEWcopy software.

Information Packets and MPC Enable Fault-tolerance in Network Control

This paper deals with fault-tolerant control of a network controlled systems (NCS) problem, where the sensors, actuator and controller are inter-connected via a communication network. A procedure is proposed for controlling a system over a network using the concept of an NCS-information-packet which is an augmented vector comprising control moves and fault flags. The size of this packet is used to define a completely fault tolerant NCS. The behavior and control of this scheme is illustrated by way of an example, where the plant is being controlled over a network. Implicit in this paper is that appropriate FDI schemes exist within the set up. The software environment used is MATLABcopy and LABVIEWcopy. The results illustrate that the scheme is tolerant to faults.

The current opinion on the use of robots for landmine detection

Anti-Personal landmines are a significant barrier to economic and social development in a number of countries. Several sensors have been developed but each one will probably have to find, if it exists, a specific area of applicability, determined by technological as well as economical or even social factors, and possibly other sensors to work with using some form of sensor fusion. A significant issue concerns the safety of the deminer. For every 5000 mines removed, one deminer is killed. The design of an accurate sensor may reduce the amount of time needed to determine whether a landmine exists, but does not increase the safety of the deminer. Since the safety issues during the eradication process are of great concern, the use and integration of cheap and simple robots and sensors in humanitarian demining has been raised. This removes the deminer from close contact with mines. This paper is concerned mainly with the work that has been done in the area of robotics and landmine detection, the problems involved, the issues that have been overlooked and the future of robots in the field.

A rough set solution to a fuzzy set problem

When designing a Takagi-Sugeno (TS) fuzzy controller, reducing the number of rules makes it easier to derive a stable Lyapunov based controller using linear matrix inequalities (LMIs). The paper describes an intuitive way of reducing a fuzzy ruleset by eliminating rules that are very similar in terms of parameters and membership functions. It is then suggested that a more formal approach to this intuitive idea may be found in rough set theory.

Use of cumulative information estimations for risk assessment of heart failure patients

As a consequence of aging population and an increasing prevalence of obesity and diabetes there are more and more patients with heart failure. This leads to a lack of professionals who can treat them and to escalating costs. An interesting solution appears to be home telemonitoring with an intelligent clinical decision support system. In this paper, the use of cumulative information estimations for risk assessment of heart failure patients with such a system is analyzed. These cumulative information estimations are utilized for creation of an algorithmic model using fuzzy decision trees that combine decision trees and notions of fuzzy logic. The algorithmic model employs mutual cumulative information and relative mutual cumulative information for association of an important piece of data about the patients with a decision node. The risk assessment with the presented solution is analyzed from the point of view of minimization of life-threatening situations and minimization of costs. Comparisons with a Bayesian network method, a nearest neighbor method, and a logistic regression method show it is a promising solution.

Adaptive and Cooperative Sampling in Networked Control Systems

The basic idea of this paper is to investigate quantitatively the influence of sampling periods on fault detection performance. Then, based on it, adaptive and cooperative sampling mechanisms in networked control systems can be designed optimally by taking into account the constraint of limited bandwidth.

Classification of a real live heart failure clinical dataset- Is TAN Bayes better than other Bayes?

Real live clinical data often present itself with a number of usual challenges, such as class imbalance, high dimensionality and missing data. There is the added complexity of the data being distributed non-uniformly and skewed. Thus the performance of classical classification methods with this type of data is lower than with other types of data. Classification based on Bayes is often suggested as a better method, however, the typical assumption made for Bayes, such as variable and data distributions are not satisfied by real clinical data. This paper focuses on improving the performance of Bayesian classifiers but also on how the underlying structures of the data affects the performance. Thus this paper will focus on Bayesian methodologies, namely use of non-parametric Kernel Density Estimation (KDE) and Tree Augmented Naïve Bayes (TAN). The aim is to measure the performance on the heart failure dataset and by focusing on how the data structure improves the classification. The missing data present in the clinical heart failure datasets are replaced using two imputation methods and results compared. We also apply the imputed datasets on three classifiers including J48 (decision tree), naïve Bayesian multinomial and Bayesian network. The experiments show an improvement on the naïve Bayes using KDE, however TAN achieves significant improvement with the different missing value imputation methods. It is seen that TAN not only improves performance of the classifier, but also enhances prediction accuracy while maintaining efficiency and model simplicity.

A human motor control perspective to multiple manipulator modelling

Abstract.This paper describes the aspects involved in modelling a multi-robot system from a human motor control perspective. The human motor control system has a hierarchical and decentralised structure, and building a control system for a multi-robot system that attains human features would require a decomposable model. Decomposition of a complex robotic system is difficult due to the interactions between the subsystems, so these have to be first separated before the system is modelled. The proposed method of separating the interconnections is applied with the aid of fuzzy modelling to derive a fully decomposable model of two manipulator robots handling a common object.

Analysis of Fuzzy Decision Trees on Expert Fuzzified Heart Failure Data

The prevalence of heart failure is 2-3% of the adult population and it is expected to grow. Half of all patients diagnosed with it die within four years. To minimize life-threatening situations and to minimize costs, it is interesting to predict mortality rates for a patient with heart failure. In this paper, a fuzzy decision tree based on classification ambiguity and a fuzzy decision tree based on cumulative information estimations are presented. They are employed on a heart failure data fuzzified on the basis of medical expert knowledge. After a transformation of fuzzy decision trees, the use of medical expert knowledge allows us to create a group of fuzzy rules that is easily interpretable by medical experts. Our study shows that different types of fuzzy decision trees can have significantly different accuracy results and interpretability.