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 AvailableTRACKING AND REGRET BOUNDS FOR ONLINE ZEROTH-ORDER EUCLIDEAN AND RIEMANNIAN OPTIMIZATION(SIAM PUBLICATIONS, 2022)
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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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ItemActive Learning for Linear Parameter-Varying System Identification( 2020-05-02)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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ItemTracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation( 2020-10-01)We study numerical optimisation algorithms that use zeroth-order information to minimise time-varying geodesically-convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant cases. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalisation of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimise the algorithm’s performance. Our results illustrate how the manifold geometry in terms of the sectional curvature affects these bounds. Additionally, we provide dynamic regret bounds for this online optimisation setting. To the best of our knowledge, these are the first regret bounds even for the Euclidean version of the problem. Lastly, via numerical simulations, we demonstrate the applicability of our algorithm on an online Karcher mean problem.
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ItemObserving the Slow States of General Singularly Perturbed Systems(IEEE, 2020)This paper studies the behaviour of observers for the slow states of a general singularly perturbed system - that is a singularly perturbed system which has boundary-layer solutions that do not necessarily converge to a slow manifold. The solutions of the boundary-layer system are allowed to exhibit persistent (e.g. oscillatory) steady-state behaviour which are averaged to obtain the dynamics of the approximate slow system. It is shown that if an observer has certain properties such as asymptotic stability of its error dynamics on average, then it is practically asymptotically stable for the original singularly perturbed system.
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ItemAdaptive Scan for Atomic Force Microscopy Based on Online Optimization: Theory and Experiment(Institute of Electrical and Electronics Engineers, 2020-05-01)A major challenge in atomic force microscopy is to reduce the scan duration while retaining the image quality. Conventionally, the scan rate is restricted to a sufficiently small value in order to ensure a desirable image quality as well as a safe tip-sample contact force. This usually results in a conservative scan rate for samples that have a large variation in aspect ratio and/or for scan patterns that have a varying linear velocity. In this paper, an adaptive scan scheme is proposed to alleviate this problem. A scan line-based performance metric balancing both imaging speed and accuracy is proposed, and the scan rate is adapted such that the metric is optimized online in the presence of aspect ratio and/or linear velocity variations. The online optimization is achieved using an extremum-seeking approach, and a semiglobal practical asymptotic stability result is shown for the overall system. Finally, the proposed scheme is demonstrated via both simulation and experiment.
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ItemPractical exponential stability and closeness of solutions for singularly perturbed systems via averaging(PERGAMON-ELSEVIER SCIENCE LTD, 2021-04)This paper studies the behavior of singularly perturbed nonlinear differential equations with boundary-layer solutions that do not necessarily converge to an equilibrium. Using the average for the derivative of the slow state variables and assuming the boundary-layer solutions converge exponentially fast to a bounded set, which is possibly parameterized by the slow variable, results on the closeness of solutions of the singularly perturbed system to the solutions of the reduced average and boundary-layer systems over a finite time interval are presented. The closeness of solution error is shown to be of order O(ε) where ε is the perturbation parameter. Moreover, under the additional assumption of exponential stability of the reduced average system, practical exponential stability of the solutions of the singularly perturbed system is provided.
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ItemScan Rate Adaptation for AFM Imaging Based on Performance Metric Optimization(Institute of Electrical and Electronics Engineers (IEEE), 2020-02)Constant-force contact-mode atomic force microscopy (AFM) relies on a feedback control system to regulate the tip–sample interaction during imaging. Due to limitations in actuators and control, the bandwidth of the regulation system is typically small. Therefore, the scan rate is usually limited in order to guarantee a desirable image quality for a constant-rate scan. By adapting the scan rate online, further performance improvement is possible, and the conditions to this improvement have been explored qualitatively in a previous study for a wide class of possible scan patterns. In this article, a quantitative assessment of the previously proposed adaptive scan scheme is investigated through experiments that explore the impact of various degrees of freedom in the algorithm. Further modifications to the existing scheme are proposed and shown to improve the closed-loop performance. The flexibility of the proposed approach is further demonstrated by applying the algorithm to tapping-mode AFM.