- Electrical and Electronic Engineering - Research Publications
Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableEconomic Model Predictive Control of Water Distribution Systems with Solar Energy and BatteriesZheng, X ; Wang, Y ; Weyer, E ; Manzie, C (IEEE, 2023-01-01)
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ItemNo Preview AvailableFeasibility detection for nested codesign of hypersonic vehiclesvan der Heide, C ; Cudmore, P ; Jahn, I ; Bone, V ; Dower, PM ; Manzie, C (IEEE, 2023)
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ItemNo Preview AvailableIncentivizing Local Controllability in Optimal Trajectory PlanningSkoraczynski, AZ ; Manzie, C ; Dower, PM (IEEE, 2023-01-01)
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ItemNo Preview AvailableConstraint Handling of an Airbreathing Hypersonic Vehicle via Predictive Reference ManagementLiu, 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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ItemNo Preview AvailableCo-design of Control Strategy and Storage Size for a Water Distribution SystemWang, 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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ItemNo Preview AvailableRobust Tracking Model Predictive Control with Koopman OperatorsWang, 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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ItemControl oriented modeling of turbocharged (TC) spark ignition (SI) engineSharma, R ; Nesic, D ; Manzie, C (SAE International, 2009-01-01)
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ItemIdle speed control using linear time varying model predictive control and discrete time approximationsSharma, R ; Nesic, D ; Manzie, C (IEEE, 2010-01-01)This paper addresses the problem of idle speed control of hydrogen fueled internal combustion engine (H2ICE) using model predictive control (MPC) and sampled data control (SDC) theories. In the first step, results from SDC theory and a version of MPC are collectively employed to obtain a rigorously developed new generic control strategy. Here, a controller, based on a family of approximate discrete time models, is designed within a previously proposed framework to have guaranteed practical asymptotic stability of the exact (unknown) discrete time model. Controller design, accomplished using MPC theory, is facilitated by successive online linearizations of the nonlinear discrete time model at each sampling instant. In the second step, the technique is implemented in the idle speed control of hydrogen internal combustion engine (H2ICE). Various conditions under which this theory can be implemented are presented and their validity for idle speed control problem are discussed. Simulations are presented to illustrate the effectiveness of the control scheme.
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ItemReal time model predictive idle speed control of ultra-lean burn engines: Experimental resultsSharma, R ; Dennis, P ; Manzie, C ; Nešić, D ; Brear, MJ (IEEE, 2011-01-01)
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ItemModel Reduction of Automotive Engines using Perturbation TheorySharma, R ; Nesic, D ; Manzie, C (IEEE, 2009-01-01)In this paper, a new constructive and versatile procedure to systematically reduce the order of control oriented engine models is presented. The technique is governed by the identification of time scale separation within the dynamics of various engine state variables and hence makes extensive use of the perturbation theory. On the basis of the dynamic characteristics and the geometry of engines, two methods for model reduction are proposed. Method 1 involves collective use of the regular and singular perturbation theories to eliminate temperature dynamics and approximate them with their quasi-steady state values, while Method 2 deals with the elimination of fast pressures. The result is a library of engine models which are associated with each other on a sound theoretical basis and simultaneously allow sufficient flexibility in terms of the reduced order modeling of a variety of engines. Different assumptions under which this model reduction is justified are presented and their implications are discussed.