Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableConstraint Handling of an Airbreathing Hypersonic Vehicle via Predictive Reference Management(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 System(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 Operators(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) engine(SAE International, 2009-01-01)
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ItemIdle speed control using linear time varying model predictive control and discrete time approximations(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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ItemModel Reduction of Automotive Engines using Perturbation Theory(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.
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ItemExtremum Seeking From 1922 To 2010(IEEE, 2010)Extremum seeking is a form of adaptive control where the steady-state input-output characteristic is optimized, without requiring any explicit knowledge about this input-output characteristic other than that it exists and that it has an extremum. Because extremum seeking is model free, it has proven to be both robust and effective in many different application domains. Equally being model free, there are clear limitations to what can be achieved. Perhaps paradoxically, although being model free, extremum seeking is a gradient based optimization technique. Extremum seeking relies on an appropriate exploration of the process to be optimized to provide the user with an approximate gradient, and hence the means to locate an extremum. These observations are elucidated in the paper. Using averaging and time-scale separation ideas more generally, the main behavioral characteristics of the simplest (model free) extremum seeking algorithm are established.
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ItemA UNIFYING FRAMEWORK FOR ANALYSIS AND DESIGN OF EXTREMUM SEEKING CONTROLLERS(IEEE, 2012-01-01)We summarize a unifying design approach to continuous-time extremum seeking that was recently reported by the authors. This approach is based on a feedback control paradigm that was to the best of our knowledge explicitly summarized for the first time in this form in our recent work. This paradigm covers some existing extremum seeking schemes, provides a direct link to off-line optimization and can be used as a unifying framework for design of novel extremum seeking schemes. Moreover, we show that other extremum seeking problem formulations can be interpreted using this unifying viewpoint. We believe that this unifying view will be invaluable to systematically design and analyze extremum seeking controllers in various settings.
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ItemA Unifying Approach to Extremum Seeking: Adaptive Schemes Based on Estimation of Derivatives(IEEE, 2010-01-01)A unifying, prescriptive framework is presented for the design of a family of adaptive extremum seeking controllers. It is shown how extremum seeking can be achieved by combining an arbitrary continuous optimization method (such as gradient descent or continuous Newton) with an estimator for the derivatives of the unknown steady-state reference-to-output map. A tuning strategy is presented for the controller parameters that ensures non-local convergence of all trajectories to the vicinity of the extremum. It is shown that this tuning strategy leads to multiple time scales in the closed-loop dynamics, and that the slowest time scale dynamics approximate the chosen continuous optimization method. Results are given for both static and dynamic plants. For simplicity, only single-input-single-output (SISO) plants are considered.