Electrical and Electronic Engineering - Research Publications
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ItemData Monitoring Sensor Network for Big Net Research Testbed(IEEE, 2009-01-01)Equipped with recent advances in electronics and communication, wireless sensor networks gained a rapid development to provide reliable information with higher Quality of Service (QoS) at lower costs. This paper presents a real-time tracking system developed as a part of the ISSNIP BigNet Testbed project. Here a GPS receiver was used to acquire position information of mobile nodes and GSM technology was used as the data communication media. Moreover, Google map based data visualization software was developed to locate the mobile nodes via Internet. This system can be used to accommodate various sensors, such as temperature, pressure, pH etc., and monitor the status of the nodes.
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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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ItemControl oriented modeling of turbocharged (TC) spark ignition (SI) engine(SAE International, 2009-01-01)
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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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ItemModel Reduction of Automotive Engines using Perturbation Theory(IEEE, 2009-01-01)In this paper, a new constructive and versatile procedure to systematically reduce the order of control oriented engine models is presented. The technique is governed by the identification of time scale separation within the dynamics of various engine state variables and hence makes extensive use of the perturbation theory. On the basis of the dynamic characteristics and the geometry of engines, two methods for model reduction are proposed. Method 1 involves collective use of the regular and singular perturbation theories to eliminate temperature dynamics and approximate them with their quasi-steady state values, while Method 2 deals with the elimination of fast pressures. The result is a library of engine models which are associated with each other on a sound theoretical basis and simultaneously allow sufficient flexibility in terms of the reduced order modeling of a variety of engines. Different assumptions under which this model reduction is justified are presented and their implications are discussed.
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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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ItemDynamic Practical Stabilization of Sampled-data Linear Distributed Parameter Systems(IEEE, 2009-01-01)In this paper, dynamic practical stability properties of infinite-dimensional sampled-data systems are discussed. A family of finite-dimensional discrete-time controllers are first designed to uniformly exponentially stabilize numerical approximate models that are obtained from space and time discretization. Sufficient conditions are provided to ensure that these controllers can be used to drive trajectories of infinite-dimensional sampled-data systems to a neighborhood of the origin by properly tuning the sampling period, space and time discretization parameters and choosing an appropriate filtering process for initial conditions.
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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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ItemOn emulation-based observer design for networked control systems(IEEE, 2010)We extend our recent framework developed for the emulation-based observer design for networked control systems (NCS) to larger classes of systems and scheduling protocols. Consider a continuous-time observer that satisfies some input-to-state / input-to-output stability properties with respect to measurement errors, we prove that the observation error converges semiglobally and practically with the maximum allowable transmission interval (MATI) as parameter, when it is implemented via a network governed by a Lyapunov uniformly globally asymptotically stable (UGAS) protocol. We show that our results can be used to build various observers for NCS such as circle criterion observers. The stability analyses rely on trajectory based small gain theorems for parametrized systems.
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ItemUniform stability of sets for difference inclusions under summability criteria(IEEE, 2009)We present equivalent characterizations of uniform global exponential stability and uniform global asymptotic stability of arbitrary closed not necessarily compact sets for nonlinear difference inclusions. Our conditions are established in the form of summability criteria that do not require the knowledge of a Lyapunov function.