Electrical and Electronic Engineering - Research Publications
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ItemPWM hybrid control systems: averaging tools for analysis and design(IEEE, 2010)We consider averaging for a class of hybrid systems that are motivated by Pulse Width Modulated (PWM) implementation of hybrid control laws for general nonlinear plants. Rapid time variations in the flow map of a hybrid system generate solutions that are also solutions of a time-invariant average hybrid system that is slightly perturbed. Results relating solutions of the time-varying system to solutions of the average system ensue. In the absence of finite escape times for the average system, on compact time domains each solution of the time-varying system is close to a solution of the average system. In the presence of asymptotic stability for the average system, the time-varying system exhibits semi-global, practical asymptotic stability. These results rely on mild regularity properties for the average system. In particular, the average system is not required to exhibit unique solutions. Both periodic and non-periodic flow maps are considered.
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ItemLyapunov functions for L-2 and input-to-state stability in a class of quantized control systems(IEEE, 2011-01-01)ℒ 2 and input-to-state stability (ISS) properties of a class of linear quantized control systems are considered. The quantized control system differs slightly from the ones considered in the literature previously. A recently proposed hybrid modeling framework and corresponding Lyapunov analysis tools are used to calculate the finite gains of the closed loop system.
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ItemIdle speed control using linear time varying model predictive control and discrete time approximations(IEEE, 2010-01-01)This paper addresses the problem of idle speed control of hydrogen fueled internal combustion engine (H2ICE) using model predictive control (MPC) and sampled data control (SDC) theories. In the first step, results from SDC theory and a version of MPC are collectively employed to obtain a rigorously developed new generic control strategy. Here, a controller, based on a family of approximate discrete time models, is designed within a previously proposed framework to have guaranteed practical asymptotic stability of the exact (unknown) discrete time model. Controller design, accomplished using MPC theory, is facilitated by successive online linearizations of the nonlinear discrete time model at each sampling instant. In the second step, the technique is implemented in the idle speed control of hydrogen internal combustion engine (H2ICE). Various conditions under which this theory can be implemented are presented and their validity for idle speed control problem are discussed. Simulations are presented to illustrate the effectiveness of the control scheme.
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ItemNew Stability Criteria for Switched Time-Varying Systems: Output-Persistently Exciting Conditions(IEEE, 2011-01-01)This paper proposes three tools to facilitate the verification of the output-persistently exciting (OPE) condition and simultaneously, provides new asymptotic stability criteria for uniformly globally stable switched systems. By introducing some related reference systems, the OPE condition of the original system can be reduced or simplified. Both the ideas of classic LaSalle invariance principle and nested Matrosov theorem are used to generate such reference systems. The effectiveness and flexibility of the proposed methods are demonstrated by two applications. From these applications, it can be seen that the flexibility of the proposed method produces a novel set of tools for checking uniform asymptotic stability of switched time-varying systems.
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ItemExtremum Seeking From 1922 To 2010(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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ItemAn improved Matrosov theorem for hybrid time-varying systems: A behavior approach(IEEE, 2011-08-29)This paper concerns the stability of hybrid time-varying systems. A behavior approach is used to transfer the functional space on hybrid time domains into a functional space on continuous-time domains. Then a new stability criterion is derived for the transferred continuous-time functional space to derive a nested Matrosov theorem for hybrid time-varying systems. The proposed Matrosov theorem does not require that the equilibrium set is compact, which indicates an extension of current results in literature. The obtained results have also a potential to be used in stability analysis for other types of dynamic systems such as time-delay systems.
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ItemA UNIFYING FRAMEWORK FOR ANALYSIS AND DESIGN OF EXTREMUM SEEKING CONTROLLERS(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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ItemA Unifying Approach to Extremum Seeking: Adaptive Schemes Based on Estimation of Derivatives(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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ItemMultivariable Newton-Based Extremum Seeking(IEEE, 2011)We present a Newton-based extremum seeking algorithm for the multivariable case. The design extends the recent Newton-based extremum seeking algorithms for the scalar case and introduces a dynamic estimator of the Hessian matrix that removes the difficulty with the possible singularity of this matrix estimate. This estimator has the form of a differential Riccati equation. We prove local stability of the new algorithm for general nonlinear dynamic systems using averaging and singular perturbations. In comparison with the standard gradient-based multivariable extremum seeking, the proposed algorithm removes the dependence of the convergence rate on the unknown Hessian matrix and makes the convergence rate, of both the parameter estimates and of the estimates of the Hessian inverse, user-assignable. In particular, the new algorithm allows all the parameters to converge with the same speed, even with maps that have highly elongated level sets. In the parameter space, the new algorithms produces trajectories straight to the extremum, as opposed to non-direct “steepest descent” trajectories. Simulation results show the advantage of the proposed approach over gradient-based extremum seeking.