Electrical and Electronic Engineering - Research Publications
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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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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.
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ItemRobustness analysis of nonlinear observers for the slow variables of singularly perturbed systems(Wiley, 2020-09-25)Estimation of unmeasured variables is a crucial objective in a broad range of applications. However, the estimation process turns into a challenging problem when the underlying model is nonlinear and even more so when additionally it exhibits multiple time scales. The existing results on estimation for systems with two time scales apply to a limited class of nonlinear plants and observers. We focus on analyzing nonlinear observers designed for the slow state variables of nonlinear singularly perturbed systems. Moreover, we consider the presence of bounded measurement noise in the system. We generalize current results by considering broader classes of plants and estimators to cover reduced-order, full-order, and higher-order observers. First, we show that the singularly perturbed system has bounded solutions under an appropriate set of assumptions on the corresponding boundary layer and reduced systems. We then exploit this property to prove that, under reasonable assumptions, the error dynamics of the observer designed for the reduced system are semiglobally input-to-state practically stable when the observer is implemented on the original plant. We also conclude (Formula presented.) stability results when the measurement noise belongs to (Formula presented.). In the absence of measurement noise, we state results on semiglobal practical asymptotical stability for the error dynamics. We illustrate the generality of our main results through three classes of systems with corresponding observers and one numerical example.
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ItemTuning of multivariable model predictive controllers through expert bandit feedback(TAYLOR & FRANCIS LTD, 2021-10-03)For certain industrial control applications an explicit function capturing the non-trivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimisation algorithm that can efficiently deal with a large number of variables, is used for optimising the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.