Electrical and Electronic Engineering - Research Publications

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    Extremum Seeking From 1922 To 2010
    Tan, Y ; Moase, WH ; Manzie, C ; Nesic, D ; Mareels, IMY ; Chen, J (IEEE, 2010-01-01)
    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 FRAMEWORK FOR ANALYSIS AND DESIGN OF EXTREMUM SEEKING CONTROLLERS
    Nesic, D ; Tan, Y ; Manzie, C ; Mohammadi, A ; Moase, W (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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    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.
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    Trajectory-based proofs for sampled-data extremum seeking control
    KHONG, S ; Nesic, D ; Tan, Y ; Manzie, CG (IEEE, 2013)
    Extremum seeking of nonlinear systems based on a sampled-data control law is revisited. It is established that under some generic assumptions, semi-global practical asymptotically stable convergence to an extremum can be achieved. To this end, trajectory-based arguments are employed, by contrast with Lyapunov-function-type approaches in the existing literature. The proof is simpler and more straightforward; it is based on assumptions that are in general easier to verify. The proposed extremum seeking framework may encompass more general optimisation algorithms, such as those which do not admit a state-update realisation and/or Lyapunov functions. Multi-unit extremum seeking is also investigated within the context of accelerating the speed of convergence.
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    On a Shubert Algorithm-Based Global Extremum Seeking Scheme
    Nesic, D ; Nguyen, T ; Tan, Y ; Manzie, C (IEEE, 2012)
    This paper adapts the so-called Shubert algorithm for Extremum Seeking Control (ESC) to seek the global extremum (in presence of local extrema) of general dynamic plants. Different from derivative based methods that are widely used in ESC, the Shubert algorithm is a good representative of sampling optimization methods. With knowledge of the Lipschitz constant of an unknown static mapping, this deterministic algorithm seeks the global extremum. By introducing “waiting time” the proposed Shubert algorithm-based global extremum seeking guarantees the semi-global practical convergence (in the initial states) to the global extremum if compact sets of inputs are considered. Several numerical examples demonstrate how proposed method may be successfully deployed.
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    On sampled-data extremum seeking control via stochastic approximation methods
    Khong, SZ ; Tan, Y ; Nesic, D ; Manzie, C (IEEE, 2013-01-01)
    This note establishes a link between stochastic approximation and extremum seeking of dynamical nonlinear systems. In particular, it is shown that by applying classes of stochastic approximation methods to dynamical systems via periodic sampled-data control, convergence analysis can be performed using standard tools in stochastic approximation. A tuning parameter within this framework is the period of the synchronised sampler and hold device, which is also the waiting time during which the system dynamics settle to within a controllable neighbourhood of the steady-state input-output behaviour. Semiglobal convergence with probability one is demonstrated for three basic classes of stochastic approximation methods: finite-difference, random directions, and simultaneous perturbation. The tradeoff between the speed of convergence and accuracy is also discussed within the context of asymptotic normality of the outputs of these optimisation algorithms.
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    Extremum Seeking Methods for Online Automotive Calibration
    Manzie, C ; Moase, W ; Shekhar, R ; Mohammadi, A ; Nesic, D ; Tan, Y ; Waschl, H ; Kolmanovsky, I ; Steinbuch, M ; del Re, L (Springer, 2014-01-01)
    The automotive calibration process is becoming increasingly difficult as the degrees of freedom in modern engines rises with the number of actuators. This is coupled with the desire to utilise alternative fuels to gasoline and diesel for the promise of lower CO2 levels in transportation. However, the range of fuel blends also leads to variability in the combustion properties, requiring additional sensing and calibration effort for the engine control unit (ECU). Shifting some of the calibration effort online whereby the engine controller adjusts its operation to account for the current operating conditions may be an effective alternative if the performance of the controller can be guaranteed within some performance characteristics. This tutorial chapter summarises recent developments in extremum seeking control, and investigates the potential of these methods to address some of the complexity in developing fuel-flexible controllers for automotive powertrains.
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    Multi-agent gradient climbing via extremum seeking control
    Kong, SZ ; Manzie, CG ; Tan, Y ; Nesic, D (IFAC - International Federation of Automatic Control, 2014)
    A unified framework based on discrete-time gradient-based extremum seeking control is proposed to localise an extremum of an unknown scalar field distribution using a group of equipped with sensors. The controller utilises estimates of gradients of the field from local dithering sensor measurements collected by the mobile agents. It is assumed that distributed coordination which ensures uniform asymptotic stability with respect to a prescribed formation of the agents is employed. The framework is useful in that a broad range of nonlinear programming algorithms can be combined with a wide class of cooperative control laws to perform extreme source seeking. Semi-global practical asymptotically stable convergence to local extrema is established in the presence of bounded field sampling noise.
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    Multidimensional global extremum seeking via the DIRECT method
    Khong, SZ ; Manzie, C ; Nesic, D ; Tan, Y (IEEE, 2012-01-01)
    This paper adapts the DIRECT method with a modified termination criterion for global extremum seeking control of multivariable dynamical plants - DIRECT is a sampling type global optimisation method for Lipschitz-continuous functions defined over compact multidimensional domains. Finite-time semi-global practical convergence is established based on a deadbeat sampled-data control law, whose sampling period is a parameter which determines the region and accuracy of convergence. A crucial part of the development is dedicated to a robustness analysis of the DIRECT method against bounded additive perturbations on the objective function. A numerical example of global extremum seeking in the presence of local extrema based on DIRECT is presented at the end.
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    Non-local stability of a multi-variable extremum-seeking scheme
    Moase, WH ; Tan, Y ; Nešić, D ; Manzie, C (IEEE, 2011-12-01)
    This paper considers non-local stability of a simple extremum-seeking (ES) scheme acting on a multiple-input single-output (MISO) plant. The scheme acts to approximately minimise the plant output, utilising a vector of periodic dithers to locally explore a map of the steady-state plant response. In a similar fashion to a previous result for single-input single-output (SISO) plants, semi-global practical asymptotic (SPA) stability (with respect to the design parameters) is demonstrated for the closed-loop system. In order to achieve this result, the dither is required to satisfy a condition similar to persistency of excitation (PE) conditions appearing in the adaptive control literature. A variety of dithers satisfying and failing to satisfy this condition are discussed. A simulation example is used to demonstrate these results.