Mechanical Engineering - Research Publications

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    Stability of Nonlinear Systems with Two Time Scales Over a Single Communication Channel
    Wang, W ; Maass, AI ; Nešić, D ; Tan, Y ; Postoyan, R ; Heemels, WPMH (IEEE, 2023-01-01)
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    Averaging for nonlinear systems on Riemannian manifolds
    Taringoo, F ; Nesic, D ; Tan, Y ; Dower, PM (IEEE, 2013)
    This paper provides a derivation of the averaging methods for nonlinear time-varying dynamical systems defined on Riemannian manifolds. We extend the results on ℝ n to Riemannian manifolds by employing the language of differential geometry.
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    Extremum seeking control for nonlinear systems on compact Riemannian manifolds
    Taringoo, F ; Nesic, D ; Tan, Y ; DOWER, PM (IEEE Press, 2014)
    This paper formulates the extremum seeking control problem for nonlinear dynamical systems which evolve on Riemannian manifolds and presents stability results for a class of numerical algorithms defined in this context. The results are obtained based upon an extension of extremum seeking algorithms in Euclidean spaces and a generalization of Lyapunov stability theory for dynamical systems defined on Rimannian manifolds. We employ local properties of Lyapunov functions to extend the singular perturbation analysis on Riemannian manifolds. Consequently, the results of the singular perturbation on manifolds are used to obtain the convergence of extremum seeking algorithms for dynamical systems on Riemannian manifolds.
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    Coordination of blind agents on Lie groups
    Taringoo, F ; Nesic, D ; DOWER, P ; Tan, Y (IEEE, 2015)
    This paper presents an algorithm for the synchronization of blind agents evolving on a connected Lie group. We employ the method of extremum seeking control for nonlinear dynamical systems defined on connected Riemannian manifolds to achieve the synchronization among the agents. This approach is independent of the underlying graph of the system and each agent updates its position on the connected Lie group by only receiving the synchronization cost function.
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    Closeness of solutions and averaging for nonlinear systems on Riemannian manifolds
    Taringoo, F ; Nesic, D ; Tan, Y ; Dower, PM (IEEE, 2013)
    An averaging result for periodic dynamical systems evolving on Euclidean spaces is extended to those evolving on (differentiable) Riemannian manifolds. Using standard tools from differential geometry, a perturbation result for time-varying dynamical systems is developed that measures closeness of trajectories via a suitable metric on a finite time horizon. This perturbation result is then extended to bound excursions in the trajectories of periodic dynamical systems from those of their respective averages, on an infinite time horizon, yielding the specified averaging result. Some simple examples further illustrating this result are also presented.
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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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    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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    Open Problems in Reset Control
    Zhao, G ; Nesic, D ; Tan, Y ; Wang, J (IEEE, 2013-01-01)
    It is well-known that there are fundamental performance limitations in the design of linear feedback control systems for single-input-single-output (SISO) linear-time-invariant (LTI) plants. These performance limitations sometimes include overshoot and rise time. This paper shows that for some examples of SISO LTI systems, it is possible to find suitable reset controllers that can overcome such performance limitations, though there are still some robust and implementable issues that need to be solved. This naturally leads to the formulation of several open research problems that we specify.