Electrical and Electronic Engineering - Research Publications

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    TRACKING AND REGRET BOUNDS FOR ONLINE ZEROTH-ORDER EUCLIDEAN AND RIEMANNIAN OPTIMIZATION
    Maass, A ; Manzie, C ; Nesic, D ; Manton, JH ; Shames, I (SIAM PUBLICATIONS, 2022)
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    An algorithm for the selection of linearisation points in non-linear systems: a diesel air-path case study
    Ahmadizadeh, S ; Maass, A ; Manzie, C ; Shames, I (TAYLOR & FRANCIS LTD, 2023-12-02)
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    Zeroth-Order Optimization on Subsets of Symmetric Matrices With Application to MPC Tuning
    Maass, A ; Manzie, C ; Shames, I ; Nakada, H (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-11-08)
    This article provides a zeroth-order optimization framework for nonsmooth and possibly nonconvex cost functions with matrix parameters that are real and symmetric. We provide complexity bounds on the number of iterations required to ensure a given accuracy level for both the convex and nonconvex cases. The derived complexity bounds for the convex case are less conservative than available bounds in the literature since we exploit the symmetric structure of the underlying matrix space. Moreover, the nonconvex complexity bounds are novel for the class of optimization problems that we consider. The utility of the framework is evident in the suite of applications that use symmetric matrices as tuning parameters. Of primary interest here is the challenge of tuning the gain matrices in model predictive controllers, as this is a challenge known to be inhibiting the industrial implementation of these architectures. To demonstrate the framework, we consider the problem of MIMO diesel air-path control and implement the framework iteratively ``in-the-loop'' to reduce tracking error on the output channels. Both simulations and experimental results are included to illustrate the effectiveness of the proposed framework over different engine drive cycles.
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    Exploiting Structure in the Bottleneck Assignment Problem
    Khoo, M ; Wood, TA ; Manzie, C ; Shames, I (ELSEVIER, 2020)
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    Tuning of multivariable model predictive controllers through expert bandit feedback
    Ira, AS ; Manzie, C ; Shames, I ; Chin, R ; Nesic, D ; Nakada, H ; Sano, T (TAYLOR & FRANCIS LTD, 2021-10-03)
    For certain industrial control applications an explicit function capturing the non-trivial 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 optimisation algorithm that can efficiently deal with a large number of variables, is used for optimising 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.