Electrical and Electronic Engineering - Research Publications

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    Constraint Handling of an Airbreathing Hypersonic Vehicle via Predictive Reference Management
    Liu, V ; Manzie, C ; Dower, PM (IEEE, 2022-01-01)
    In this paper we consider the problem of constraint handling for an airbreathing hypersonic vehicle (HSV) through a hierarchical control architecture. A reference manager is incorporated as an intermediate control loop whose role is to modify an offline generated reference trajectory, without knowledge of disturbances, to enforce state and input constraints. Compared with traditional constraint handling approaches in HSV literature, this proposed approach allows for the deployment of controllers that are not typically formulated to handle constraints. We provide a computation time and constraint management comparison between a scheme that directly utilizes the nonlinear vehicle model and one that performs online linearization of the model.
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    Wireless Network Simulation to Create Machine Learning Benchmark Data
    Katzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C ; Kopacz, J (IEEE, 2022-01-01)
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    A Learning-Based Approach to Approximate Coded Computation
    Agrawal, N ; Qiu, Y ; Frey, M ; Bjelakovic, I ; Maghsudi, S ; Stanczak, S ; Zhu, J (IEEE, 2022-01-01)
    Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.
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    Regularizing policy iteration for recursive feasibility and stability
    Granzotto, M ; de Silva, OL ; Postoyan, R ; Nesic, D ; Jiang, Z-P (IEEE, 2022)
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    Towards improving the estimation performance of a given nonlinear observer: a multi-observer approach
    Petri, E ; Postoyan, R ; Astolfi, D ; Nesic, D ; Andrieu, V (IEEE, 2022-01-01)
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    Co-design of Control Strategy and Storage Size for a Water Distribution System
    Wang, Y ; Weyer, E ; Manzie, C ; Simpson, AR (IEEE, 2022-01-01)
    The design and operation of water distribution systems (WDSs) are two interrelated tasks that both impact the overall cost of the systems. The traditional approach is to first design the system and then develop a control strategy for the specified infrastructure. However, this is suboptimal in that the controlled system may hit operating constraints arising from inadequate design, or the capital cost may be excessive due to conservative design processes. The challenge of designing both the infrastructure and control strategy simultaneously is amplified by the demand profiles and energy prices being stochastic. In this paper, we investigate stochastic co-design optimization problems for simultaneously optimizing the tank size and parameters of a pumping strategy. We employ Markov chain theory to establish tractable co-design optimization problems. We show several simulation results to demonstrate the efficacy of the proposed approach.
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    Robust Tracking Model Predictive Control with Koopman Operators
    Wang, Y ; Yang, Y ; Pu, Y ; Manzie, C (IEEE, 2022-01-01)
    Koopman operators can be used to lift nonlinear dynamics into a higher dimensional space to obtain a linear model with nonlinear basis functions. They have proven particularly attractive when combined with data-driven techniques to identify the basis function coefficients. The resulting higherorder linear model is subsequently a good candidate for MPC application, as convex solvers may be applied in the lifted space. Nonetheless, the modeling errors between the original nonlinear system and the approximated Koopman linear model must be taken into account in the MPC design such that the closed-loop properties such as recursive feasibility and convergence can be guaranteed. In this paper, we use a robust constraint tightening approach to address this issue. To demonstrate the approach, we apply the proposed robust Koopman tracking MPC (KTMPC) to a continuous stirred tank reactor case study to show its efficacy.
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    Local Intrinsic Dimensionality Signals Adversarial Perturbations
    Weerasinghe, S ; Abraham, T ; Alpcan, T ; Erfani, SM ; Leckie, C ; Rubinstein, BIP (IEEE, 2022)
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    Improved Pump Setpoint Selection Using a Calibrated Hydraulic Model of a High-Pressure Irrigation System
    Wang, Y ; Zhao, Q ; Wu, W ; Willis, A ; Simpson, AR ; Weyer, E (Elsevier, 2022-01-01)
    This paper presents a case study of the operational management of the Robinvale high-pressure piped irrigation water delivery system (RVHPS) in Australia. Based on datasets available, improved pump setpoint selection using a calibrated hydraulic model is investigated. The first step was to implement pre-processing of measured flow and pressure data to identify errors in the data and possible faulty sensors. An EPANET hydraulic simulation model was updated with calibrated pipe roughness height values by using the processed pressure and flow data. Then, new pump setpoints were selected using the calibrated model given the actual measured demands such that the pressures in the network were minimized subject to required customer service standards. Based on a two-day simulation, it was estimated that 4.7% savings in pumping energy cost as well as 4.7% reduction in greenhouse gas emissions can be achieved by applying the new pump setpoints.