Electrical and Electronic Engineering - Research Publications

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Start date: 2010 × End date: 2010 × Author: Nesic, Dragan × Author: Manzie, Christopher × Author: Moase, William ×

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    Extremum Seeking From 1922 To 2010
    Tan, Y ; Moase, WH ; Manzie, C ; Nesic, D ; Mareels, IMY ; Chen, J (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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    A Unifying Approach to Extremum Seeking: Adaptive Schemes Based on Estimation of Derivatives
    Nesic, D ; Tan, Y ; Moase, WH ; Manzie, C (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.