Electrical and Electronic Engineering - Research Publications

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Start date: 2010 × Author: Manzie, Christopher × Author: Shames, Iman × Author: Maass Martinez, Alejandro × Author: Chin, Robert ×

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    Active Learning for Linear Parameter-Varying System Identification
    Chin, R ; Maass, A ; Ulapane, N ; Manzie, C ; Shames, I ; Nesic, D ; Rowe, JE ; Nakada, H (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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    Tuning of model predictive engine controllers over transient drive cycles
    Maass, A ; Manzie, C ; Shames, I ; Chin, R ; Nesic, D ; Ulapane, N ; Nakada, H (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.