A minimal realization technique for the dynamical structure function of a class of LTI systems

in IEEE Transactions on Control of Network Systems (in press)

Data driven discovery of cyber physical systems

in Nature Communications (2019)

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved ... [more ▼]

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber- physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance. [less ▲]

Robust stability analysis of active voltage control for high-power IGBT switching by Kharitonov's theorem

in IEEE Transactions on Power Electronics (2016), 31(3), 2584-2595

Identifying Biochemical Reaction Networks From Heterogeneous Datasets

in IEEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December)

Online Fault Diagnosis for Nonlinear Power Systems

in Automatica (2015), 55

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisationbased formulation of the fault identification problem. An iterative reweighted `1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲]

Modelling the natural history of Huntingtons disease progression

in BMJ Open (2014)

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we ... [more ▼]

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. Methods : Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington’s Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. Results : Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89–91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. Conclusions : This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders. [less ▲]

H2 Norm Based Network Volatility Measures

in The proceedings of the American Control Conference (2014)

Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical ... [more ▼]

Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical links in a network, where relatively small improvements can lead to large reductions in network volatility measures. We also examine volatility measures of individual nodes and their dependence on the topological position in the network. Finally, we investigate the dependence of the volatility on different network interconnections, weights of the edges and other network properties. Hence in an intuitive and efficient way, we can identify critical links, nodes and interconnections in network which can shed light in the network design to make it more robust. [less ▲]

Finite-time road grade computation for a vehicle platoon

in IEEE (2014)

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the ... [more ▼]

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the road grade in a finite number of time-steps by using only its own successive velocity measurements. Simulations then illustrate the theoretical results. These new results can be applied to real-world vehicle platooning problems to reduce fuel consumption and carbon dioxide emissions. [less ▲]

Decentralised minimum-time consensus

in Automatica (2013), 49(5), 1227-1235

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically ... [more ▼]

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the final consensus value of the whole network in finite time using the minimum number of successive values of its own state history. We show that the minimum number of steps is related to a Jordan block decomposition of the network dynamics, and present an algorithm to compute the final consensus value in the minimum number of steps by checking a rank condition of a Hankel matrix of local observations. Furthermore, we prove that the minimum number of steps is related to graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the minimum external equitable partition. [less ▲]

Real-time Fault Diagnosis for Large-Scale Nonlinear Power Networks

in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013)

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisation-based formulation of the fault identification problem. An iterative reweighted ℓ1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲]