Electrical and Electronic Engineering - Research Publications

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Start date: 2010 × Author: Nesic, Dragan × Type: Journal Article ×

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Now showing 1 - 10 of 111
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    Decentralized event-triggered estimation of nonlinear systems
    Petri, E ; Postoyan, R ; Astolfi, D ; Nešić, D ; Heemels, WPMH (Elsevier BV, 2024-02-01)
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    Stability analysis of optimal control problems with time-dependent costs?
    Benahmed, S ; Postoyan, R ; Granzotto, M ; Busoniu, L ; Daafouz, J ; Nesic, D (PERGAMON-ELSEVIER SCIENCE LTD, 2023-11)
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    TRACKING AND REGRET BOUNDS FOR ONLINE ZEROTH-ORDER EUCLIDEAN AND RIEMANNIAN OPTIMIZATION
    Maass, A ; Manzie, C ; Nesic, D ; Manton, JH ; Shames, I (SIAM PUBLICATIONS, 2022)
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    Event-Triggered Control Through the Eyes of a Hybrid Small-Gain Theorem
    Maass, AI ; Wang, W ; Nesic, D ; Postoyan, R ; Heemels, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023-10)
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    Learning-Based Adaptive Control for Stochastic Linear Systems With Input Constraints
    Siriya, S ; Zhu, J ; Nesic, D ; Pu, Y (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)
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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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    Online optimization of spark advance in alternative fueled engines using extremum seeking control
    Mohammadi, A ; Manzie, C ; Nesic, D (Elsevier, 2014-08-01)
    Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This makes optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of grey-box extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. Since practical implementation of grey-box extremum seeking methods is typically done using digital technology, this paper takes advantage of emulation design methods to port the existing continuous-time grey-box extremum seeking methods to discrete-time frameworks. The ability and flexibility of the proposed discrete-time framework is demonstrated through simulations and in practical situation using a natural gas fueled engine.
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    Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters
    Chong, M ; Postoyan, R ; Nesic, D ; Kuhlmann, L ; Varsavsky, A (IOP PUBLISHING LTD, 2012-04)
    We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.
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    Sampled Data Model Predictive Idle Speed Control of Ultra-Lean Burn Hydrogen Engines
    Sharma, R ; Nesic, D ; Manzie, C (Institute of Electrical and Electronics Engineers, 2013-03-01)
    A model-based approach for the idle speed control of ultra-lean burn engines is presented. The results from model predictive control (MPC) are extended and collectively used with the existing sampled data control theory to obtain a rigorously developed idle speed control strategy. Controller is designed using MPC theory and facilitated by successive online linearizations of the nonlinear discrete-time model at each sampling instant. Simultaneously, the approximations due to the discretization of the nonlinear engine model are explicitly considered by designing the control within a previously proposed control design framework to obtain appropriate stability guarantees of an exact (unknown) discrete-time engine model. The proposed idle speed control method is experimentally validated on a prototype 6-cylinder hydrogen engine.