Mechanical Engineering - Research Publications
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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 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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ItemNon-local stability of a multi-variable extremum-seeking scheme(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.
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ItemA Systematic Approach to Extremum Seeking Based on Parameter Estimation(IEEE, 2010-01-01)We present a systematic approach for design of extremum seeking (ES) controllers for a class of uncertain plants that are parameterized with unknown parameters. First, we present results for static plants and show how it is possible to combine, under certain general conditions, an arbitrary optimization method with an arbitrary parameter estimation method in order to obtain extremum seeking. Our main results also specify how controller needs to be tuned in order to achieve extremum seeking. Then, we consider dynamic plants and separate our results into the stable plant case and unstable plant case. For each of these cases, we present conditions on general plants, controllers, observers, parameter estimators and optimization algorithms that guarantee semi-global practical convergence to the extremum when controller parameters are tuned appropriately. Our results apply to general nonlinear plants with multiple inputs and multiple parameters.
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ItemModel Reduction of Turbocharged (TC) Spark Ignition (SI) Engines(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011-03-01)This paper proposes a new procedure to reduce the order of control oriented turbocharged (TC) spark ignition (SI) engine models. The starting point of this work is a higher dimensional, fully validated model defined which is not appropriate for control design. The model reduction technique is based on the identification of time scale separation within the dynamics of various engine state variables with pertinent use of perturbation theory. The model reduction is accomplished in two steps and exploits the dynamic and physical characteristics of engine design and operation. In the first step, regular and singular perturbation theories are collectively employed to eliminate temperature dynamics and replace them with their quasi-steady state values. This is followed by the elimination of fast pressures. As a result, a library of engine models is obtained which are associated with each other on a sound theoretical basis and at the same time allow sufficient flexibility in terms of the reduced order modeling. Different assumptions under which this model reduction is justified are presented and their implications are discussed. The approximating properties of the proposed engine models with respect to the original higher dimensional model are quantitatively assessed through comprehensive simulations.
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ItemPredictors of short and long term outcome in patellofemoral pain syndrome: a prospective longitudinal study(BMC, 2010-01-19)BACKGROUND: Patellofemoral pain syndrome (PFP) is a common musculoskeletal condition that has a tendency to become chronic and problematic in a proportion of affected individuals. The objective of this study was to identify prognostic factors that may have clinical utility in predicting poor outcome on measures of pain and function in individuals with PFP. METHODS: A prospective follow-up study was conducted of 179 participants in a randomised clinical trial. Nine baseline factors (age, gender, body mass index, arch height, duration of knee pain, worst pain visual analogue scale, Kujala Patellofemoral Score (KPS), functional index questionnaire (FIQ), step down repetitions) were investigated for their prognostic ability on outcome assessed at six, 12 and 52 weeks (worst pain, KPS and FIQ). Factors with significant univariate associations were entered into multivariate linear regression models to identify a group of factors independently associated with poor outcome. RESULTS: Long symptom duration was the most consistent predictor of poor outcome over 52 weeks rated on the KPS and the FIQ (beta-0.07, 95% confidence interval -0.1 to -0.03, p < 0.000; and -0.02, -0.03 to -0.01, p < 0.000, respectively). Worse KPS at baseline was predictive of outcome at six, 12 and 52 weeks. Gender, body mass index and arch height were generally not associated with outcome (univariate analysis), while age, worst pain, FIQ and step downs were excluded during multivariate analyses. CONCLUSIONS: Patients presenting with PFP of long duration who score worse on the KPS have a poorer prognosis, irrespective of age, gender and morphometry. These results suggest that strategies aimed at preventing chronicity of more severe PFP may optimise prognosis.