Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableTRACKING AND REGRET BOUNDS FOR ONLINE ZEROTH-ORDER EUCLIDEAN AND RIEMANNIAN OPTIMIZATION(SIAM PUBLICATIONS, 2022)
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ItemNo Preview AvailableEvent-Triggered Control Through the Eyes of a Hybrid Small-Gain Theorem(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023-10)
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ItemNo Preview AvailableOn state estimation for nonlinear systems under random access wireless protocols(SPRINGER LONDON LTD, 2023-03-01)This article is dedicated to Eduardo D. Sontag on the occasion of his 70th birthday. We build upon fundamental stability concepts developed by Sontag, such as input-to-state stability and its related properties, to study a relevant application in industrial internet of things, namely estimation for wireless networked control systems. Particularly, we study emulation-based state estimation for nonlinear plants that communicate with a remote observer over a shared wireless network subject to packet losses. To reduce bandwidth usage, a stochastic communication protocol is employed to determine which node should be given access to the network. Each node has a different successful transmission probability. We describe the overall closed-loop system as a stochastic hybrid model, which allows us to capture the behaviour both between and at transmission instants, whilst covering network features such as random transmission instants, packet losses and stochastic scheduling. We then provide sufficient conditions on the transmission rate that guarantee an input-to-state stability property (in expectation) for the corresponding estimation error system. We illustrate our results in the design of circle criterion observers.
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ItemActive Learning for Linear Parameter-Varying System Identification( 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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ItemTracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation( 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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ItemA hybrid model of networked control systems implemented on WirelessHART networks under source routing configuration(IEEE, 2016)A Network control system (NCS) is a control system in which communication between subsystems takes place over a digital network. Numerous results exist in the literature on modelling, analysis and design of NCSs in the presence of specific communication constraints such as packet dropouts, delays, data rates, quantization, etc. However, when analysing NCSs implemented on real physical networks, the existing results are based on restrictive assumptions. We consider NCSs over WirelessHART, the first international standard for industrial process control. With the goal of closing the gap between theory and practice, we propose for the first time a hybrid control-oriented model of WirelessHART NCSs under source routing configuration. Moreover, asymptotic and exponential stability results are presented under reasonable conditions.
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ItemStochastic stabilisation and power control for nonlinear feedback loops communicating over lossy wireless networks(IEEE, 2020)We study emulation-based stabilisation of nonlinear networked control systems communicating over multiple wireless channels subject to packet loss. Specifically, we establish sufficient conditions on the rate of transmission that guarantee Lp stability-in-expectation of the overall closed-loop system. These conditions depend on the cumulative dropout probability of the network nodes for static protocols. We use the obtained stability results to study power control, where we show there are interesting trade-offs between the transmission rate, transmit power, and stability. Lastly, numerical examples are presented to illustrate our results.
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ItemState estimation of non-linear systems over random access wireless networks(IEEE, 2021-01-01)We study emulation-based state estimation for non-linear plants that communicate with a remote observer over a shared wireless network subject to packet losses. To reduce bandwidth usage, a stochastic communication protocol is employed to determine which node should be given access to the network. We describe the overall wireless system as a hybrid model, which allows us to capture the behaviour both between and at transmission instants, whilst covering network features such as random transmission instants, packet losses, and stochastic scheduling. Under this setting, we provide sufficient conditions on the transmission rate that guarantee an input-to-state stability property for the corresponding estimation error system. We illustrate our results with an example of Lipschitz non-linear plants.
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ItemActive Learning for Linear Parameter-Varying System Identification(ELSEVIER, 2020-01-01)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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ItemTuning of model predictive engine controllers over transient drive cycles(ELSEVIER, 2021-07-18)A framework for tuning the parameters of model predictive controllers (MPCs) based on gradient-free optimisation (GFO) is proposed. Efficient calibration of MPCs is often a difficult task given the large number of tuning parameters and their non-intuitive correlation with the output response. We propose an efficient and systematic framework for the tuning of MPC parameters that can be implemented iteratively within the closed-loop setting. The performance of the proposed GFO-based algorithm is evaluated through its application to air-path control for diesel engines over simulations and experiments. We illustrate that the tuned parameters provide satisfactory tracking of reference trajectories over engine drive cycles with only a few iterations. Thereby, we extend existing MPC tuning approaches that calibrate parameters using step responses on the fuel rate and engine speed onto tuning over a full drive cycle response.