Mechanical Engineering - Research Publications

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Author: Nesic, Dragan ×

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Now showing 1 - 10 of 64
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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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    A Multi-Processor Implementation for Networked Control Systems
    Maass, AI ; Wang, W ; Nesic, D ; Tan, Y ; Postoyan, R (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)
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    On state estimation for nonlinear systems under random access wireless protocols
    Maass, AI ; Nesic, D ; Postoyan, R ; Tan, Y (SPRINGER LONDON LTD, 2023-03-01)
    This article is dedicated to Eduardo D. Sontag on the occasion of his 70th birthday. We build upon fundamental stability concepts developed by Sontag, such as input-to-state stability and its related properties, to study a relevant application in industrial internet of things, namely estimation for wireless networked control systems. Particularly, we study emulation-based state estimation for nonlinear plants that communicate with a remote observer over a shared wireless network subject to packet losses. To reduce bandwidth usage, a stochastic communication protocol is employed to determine which node should be given access to the network. Each node has a different successful transmission probability. We describe the overall closed-loop system as a stochastic hybrid model, which allows us to capture the behaviour both between and at transmission instants, whilst covering network features such as random transmission instants, packet losses and stochastic scheduling. We then provide sufficient conditions on the transmission rate that guarantee an input-to-state stability property (in expectation) for the corresponding estimation error system. We illustrate our results in the design of circle criterion observers.
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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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    Control oriented modeling of turbocharged (TC) spark ignition (SI) engine
    Sharma, R ; Nesic, D ; Manzie, C (SAE International, 2009-01-01)
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    Idle speed control using linear time varying model predictive control and discrete time approximations
    Sharma, R ; Nesic, D ; Manzie, C (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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    Real time model predictive idle speed control of ultra-lean burn engines: Experimental results
    Sharma, R ; Dennis, P ; Manzie, C ; Nešić, D ; Brear, MJ (IEEE, 2011-01-01)
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    Model Reduction of Automotive Engines using Perturbation Theory
    Sharma, R ; Nesic, D ; Manzie, C (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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