Arun K Tangirala

Time-delay estimation in closed-loop systems is of critical value in the tasks of system identification, closed-loop performance assessment and process control, in general. In this work, we introduce the application of mutual information (MI) theory to estimate process delay under closed-loop conditions. The hallmark of the proposed method is that no exogenous (dither) signal is required to estimate the delay. Further, the method allows estimation of time-delays merely from the step response of the system. The method is based on the estimation of a quantity known as the average mutual information (AMI) computed between the input and output of the system. The estimation of AMI involves estimation of joint probability distribution of the input-output pair and therefore is a superset of the existing correlation-based methods, which only compute second-order moments of the joint distribution. Simulation studies are presented to demonstrate the practicality and utility of the proposed method.

Network architecture plays a crucial role in governing the dynamics of any biological network. Further, network structures have been shown to remain conserved across organisms for a given phenotype. Therefore, the mapping between network structures and the output functionality not only aids in understanding of biological systems but also finds application in synthetic biology and therapeutics. Based on the approaches involved, most of the efforts hitherto invested in this field can be classified into three broad categories, namely, computational efforts, rule-based methods and systems-theoretic approaches. The present review provides a qualitative and quantitative study of all three approaches in the light of three well-researched biological phenotypes, namely, oscillation, toggle switching, and adaptation. We also discuss the advantages, limitations, and future research scope for all three approaches along with their possible applications to other emergent properties of biological relevance.

Most of the existing flow sensors are expensive and limited in their capabilities for sensing bidirectional flow. Low-cost and accurate flow sensors with bidirectional sensing capability have numerous applications in the residential and irrigation sectors. Evaluation of a low-cost, cantilever-based sensor, suitable for measuring flow rates under turbulent flow conditions is presented in this article. Such sensors are reported for micro-fluidic applications but its potential application in large diameter pipes under turbulent flow has not been studied yet. A cantilever formed using a thin stainless-steel strip is used as the sensing element in the proposed sensor. One of the ends of the cantilever is firmly fitted to the inner wall of the pipe, and it bends or deflects towards the direction of the flow as a function of the flow rate. To experimentally evaluate the sensor in detail, the mean deflection angle of the cantilever is measured using a camera, and an image processing algorithm. In practice, the angle can be sensed using simpler methods. The performance of the prototype sensor has been evaluated after building an appropriate regression model. The results are subsequently expressed in terms of the mean flow velocity, thereby providing its potential utility in pipes of other dimensions. The shape of the mean flow velocity with respect to the mean angle of deflection characteristic of the proposed sensor matched well with the theoretical deflection computed. The sensor developed has given an accuracy of 3 % of full scale, for flow rates in the range of 2-15.5 m3/hr. The proposed sensing mechanism can realize cost-effective, simple, and reliable flow sensors. Such sensors will find applications in residential and industrial domains.

In data-driven dynamical modeling, precise estimation of the parameters of large models from limited data has been considered a challenging task. The precision of the parameter estimates is highly dependent upon the information contained in the data; Loss of practical identifiability and sloppiness in the model structure are major challenges in estimating parameters precisely and closely related to the information contained in the data. Therefore, quantifying information is an important step in data-driven modeling. Quantifying information is a well-studied problem in the frequentist approach, where Fisher Information is one of the widely used metrics. However, Fisher Information computed via maximum likelihood estimation cannot accommodate any known prior knowledge about the parameters. Prior knowledge of the parameters along with informative experiments will improve the precision of the estimates. Bayesian estimation accommodates prior information in the form of a p.d.f. There has been very little work in the literature for quantifying information in the Bayesian framework. In this work, we introduce a new method for estimating information gain in the Bayesian framework using what is known as the Bhattacharyya coefficient. It is seen that the bounds of the coefficient have an insightful interpretation naturally in terms of information gain on the parameter of interest. We also demonstrate using case studies that the information gain of each parameter is an indication of loss of practical identifiability and sloppy parameters. It is also shown that the proposed information gain can be used as a model selection tool in black-box identification.

A commonly encountered issue in process industry is concerned with the detection of plant-wide oscillations. In this paper, a new visualization tool termed as the power spectral correlation map (PSCMAP) is proposed for this purpose. The proposed colour map is based on a new measure defined as the power spectral correlation index (PSCI). A simple clustering algorithm is developed to group blocks of variables with similar spectral shapes. The combined visualization tool is shown to be simple, effective and powerful in collecting variables with common frequency-domain behaviour in a multivariate process. The potential of the combined technique is illustrated by an application to two industrial processes, (i) a simulated pulp and paper process and (ii) a SE Asia refinery. © 2005 Elsevier Ltd. All rights reserved.

Fault detection in linear systems is a fairly matured area where the well-known principal component analysis (PCA) and its variants are widely used. However, a large class of non-linear systems exist, especially chemical processes, on which such techniques cannot be applied. The present work aims at demonstrating the application of self-organising maps (SOM) for fault detection in non-linear processes. SOM belongs to the class of unsupervised and competitive learning algorithms and it is highly capable of handling nonlinear relationships. Application of SOM to fault detection involves generation of a reference template for the process under fault-free conditions. Online fault detection is performed by generating a new template using a windowing of the data, which is compared with the reference template using a novel metric based on the node weights obtained from SOM to detect possible faults in the process. Simulation studies on two non-linear systems, namely, (1) continuously stirred tank reactor (CSTR) and (2) bioreactor process demonstrate the practicality and utility of the proposed method. © 2010 Inderscience Enterprises Ltd.

Identification of output-error models for systems with multiple time scales is known to be a challenging problem due to the spread of dynamics across a wide range of time scales. The wide separation in time constants of the system forces one to deal with unusually fast sampling rates for slow dynamics. In general, output-error model identification of multiscale systems using prediction-error minimization suffers whenever they are initialized with auto-regressive exogenous (ARX) model parameter estimates owing to the sensitivity of ARX models at fast sampling. In the present work, it is observed that the classical Steglitz Mc-Bride (SM) algorithm can serve as an excellent alternative for identification of simple multiscale systems. Despite its advantages, it is seen to yield unstable models at times (related to the sensitivity of ARX estimation) and sometimes converges to a secondary optima. In this work, a multiscale SM algorithm, which respects the multiscale nature of data generating process, is proposed in order to reduce the sensitivity issues arising due to fast sampling. The proposed methodology is observed to yield good results for multiscale systems in terms of obtaining stable models and faster convergence. The performance of the proposed method is demonstrated on three simulation examples and the results are compared with traditional methods.

Buckling control of space structures using piezoelectric actuators is an emerging area of research. In particular, cylindrical shells present several challenges as they exhibit multiple buckling modes. This work focuses on placement of ring actuators on cylindrical shells exhibiting axisymmetric buckling. A new method based on the characteristic wavelength of the cylindrical shell is proposed for actuator placement. It is shown by means of numerical studies that passive control of these shells shows a distinct advantage over the conventional actuator placement. It is possible to obtain a stiffer load-axial shortening response as well as a peak load enhancement of the shell using the present approach. The results obtained give important insights into the actuator placement problem for cylindrical shells undergoing axisymmetric buckling.

The method proposed by Rojas et al.1 for the design of sliding mode controllers (SMC) for unstable first order plus time delay systems, is extended for delay-time constant ratio (ε) up to 1.8. The SMC settings obtained for various ε are fitted by simple equations. Up to ε = 1.2, the method is found to be more robust than that of latest PID Controller proposed by Padmasree et al.2 There is no method available in literature to stabilize unstable systems using PID controller for ε > 1.2. Simulation results are also given for a nonlinear bioreactor control problem.

Providing accurate and reliable travel time information to travellers is essential to improve the quality of public transit systems. With the availability of the latest technologies, it has become possible to collect a large amount of traffic data to analyze and understand these systems better. Traffic in India is characterized by lack of lane discipline and the presence of vehicles of varying static and dynamic characteristics, which makes prediction of bus travel time especially challenging. The aim of this study is to identify both a prediction algorithm that can handle high variability and suitable inputs or regressors to be used. Earlier studies performed offline manual grouping considering the patterns observed, which leads to limitations for automated field implementations. The present study explores the use of data-driven approaches, primarily clustering, to address the challenges for the prediction of bus travel time trends. Discrete wavelet transform (DWT) was used to extract trends from the travel time measurements. Three popular clustering algorithms—k-means, hierarchical, and self organizing maps (SOM)—were used to identify patterns. Travel time trends were then predicted by searching for similar cluster patterns within the historical database using pattern sequence-based forecasting (PSF). A comparison of the performance of these algorithms was carried out based on prediction errors. The clustering +prediction framework developed was also compared with the case when no clustering was done on the regressor dataset.