Electrical and Electronic Engineering - Research Publications

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    When to stop value iteration: stability and near-optimality versus computation
    Granzotto, M ; Postoyan, R ; Nešić, D ; Buşoniu, L ; Daafouz, J ( 2020-11-19)
    Value iteration (VI) is a ubiquitous algorithm for optimal control, planning, and reinforcement learning schemes. Under the right assumptions, VI is a vital tool to generate inputs with desirable properties for the controlled system, like optimality and Lyapunov stability. As VI usually requires an infinite number of iterations to solve general nonlinear optimal control problems, a key question is when to terminate the algorithm to produce a “good” solution, with a measurable impact on optimality and stability guarantees. By carefully analysing VI under general stabilizability and detectability properties, we provide explicit and novel relationships of the stopping criterion’s impact on near-optimality, stability and performance, thus allowing to tune these desirable properties against the induced computational cost. The considered class of stopping criteria encompasses those encountered in the control, dynamic programming and reinforcement learning literature and it allows considering new ones, which may be useful to further reduce the computational cost while endowing and satisfying stability and near-optimality properties. We therefore lay a foundation to endow machine learning schemes based on VI with stability and performance guarantees, while reducing computational complexity.
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    Active Learning for Linear Parameter-Varying System Identification
    Chin, R ; Maass, AI ; Ulapane, N ; Manzie, C ; Shames, I ; Nešić, D ; Rowe, JE ; Nakada, H ( 2020-05-02)
    Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.
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    Tracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation
    Maass, AI ; Manzie, C ; Nesic, D ; Manton, JH ; Shames, I ( 2020-10-01)
    We study numerical optimisation algorithms that use zeroth-order information to minimise time-varying geodesically-convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant cases. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalisation of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimise the algorithm’s performance. Our results illustrate how the manifold geometry in terms of the sectional curvature affects these bounds. Additionally, we provide dynamic regret bounds for this online optimisation setting. To the best of our knowledge, these are the first regret bounds even for the Euclidean version of the problem. Lastly, via numerical simulations, we demonstrate the applicability of our algorithm on an online Karcher mean problem.
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    On the Latency, Rate and Reliability Tradeoff in Wireless Networked Control Systems for IIoT
    Liu, W ; Nair, G ; Li, Y ; Nesic, D ; Vucetic, B ; Poor, HV ( 2020-07-01)
    Wireless networked control systems (WNCSs) provide a key enabling technique for Industry Internet of Things (IIoT). However, in the literature of WNCSs, most of the research focuses on the control perspective, and has considered oversimplified models of wireless communications which do not capture the key parameters of a practical wireless communication system, such as latency, data rate and reliability. In this paper, we focus on a WNCS, where a controller transmits quantized and encoded control codewords to a remote actuator through a wireless channel, and adopt a detailed model of the wireless communication system, which jointly considers the inter-related communication parameters. We derive the stability region of the WNCS. If and only if the tuple of the communication parameters lies in the region, the average cost function, i.e., a performance metric of the WNCS, is bounded. We further obtain a necessary and sufficient condition under which the stability region is n-bounded, where n is the control codeword blocklength. We also analyze the average cost function of the WNCS. Such analysis is non-trivial because the finite-bit control-signal quantizer introduces a non-linear and discontinuous quantization function which makes the performance analysis very difficult. We derive tight upper and lower bounds on the average cost function in terms of latency, data rate and reliability. Our analytical results provide important insights into the design of the optimal parameters to minimize the average cost within the stability region.
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    Tuning of multivariable model predictive controllersthrough expert bandit feedback
    Ira, AS ; Manzie, C ; Shames, I ; Chin, R ; Nesic, D ; Nakada, H ; Sano, T ( 2020-02-09)
    For certain industrial control applications an explicit function capturing the nontrivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimization algorithm that can efficiently deal with a large number of variables, is used for optimizing the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.
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    Utility of Pan-Family Assays for Rapid Viral Screening: Reducing Delays in Public Health Responses During Pandemics
    Erlichster, M ; Chana, G ; Zantomio, D ; Goudey, B ; Skafidas, E ( 2020)

    Summary

    Background

    The SARS-CoV-2 pandemic has highlighted deficiencies in the testing capacity of many developed countries during the early stages of emerging pandemics. Here we describe the potential for pan-family viral assays to improve early accessibility of large-scale nucleic acid testing.

    Methods

    Coronaviruses and SARS-CoV-2 were used as a case-study for investigating the utility of pan-family viral assays during the early stages of a novel pandemic. Specificity of a pan-coronavirus (Pan-CoV) assay for viral detection was assessed using the frequency of common human coronavirus (HCoV) species in key populations. A reported Pan-CoV assay was assessed to determine sensitivity to SARS-CoV-2 and 59 other coronavirus species. The resilience of the primer target regions of this assay to mutation was assessed in 8893 high quality SARS-CoV-2 genomes to predict ongoing utility during pandemic progression.

    Findings

    Due to infection with common HCoV species, a Pan-CoV assay would return a false positive for as few as 1% of asymptomatic adults, but up to 30% of immunocompromised patients displaying symptoms of respiratory disease. Two of the four reported pan-coronavirus assays would have identified SARS-CoV-2 and we demonstrate that with small adjustments to the primers, these assays can accommodate novel variation observed in animal coronaviruses. The assay target region of one well established Pan-CoV assay is highly resistant to mutation compared to regions targeted by other widely applied SARS-CoV-2 RT-PCR assays.

    Interpretation

    Pan-family assays have the potential to greatly assist management of emerging public health emergencies through prioritization of high-resolution testing or isolation measures, despite limitations in test specificity due to cross-reactivity with common pathogens. Targeting highly conserved genomic regions make pan-family assays robust and resilient to mutation of a given virus. This approach may be applicable to other viral families and has utility as part of a strategic stockpile of tests maintained to better contain spread of novel diseases prior to the widespread availability of specific assays.