Electrical and Electronic Engineering - Research Publications

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Start date: 2020 × Type: Journal Article × Author: Nesic, Dragan ×

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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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    Stable Near-Optimal Control of Nonlinear Switched Discrete-Time Systems: An Optimistic Planning-Based Approach
    Granzotto, M ; Postoyan, R ; Busoniu, L ; Nesic, D ; Daafouz, J (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-05)
    Originating in the artificial intelligence literature, optimistic planning(OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is therefore relevant for the near-optimal control of nonlinear switched systems for which the switching signal is the control, and no continuous input is present. However, OP exhibits several limitations, which prevent its desired application in a standard control engineering context, as it requires for instance that the stage cost takes values in [0,1], an unnatural prerequisite, and that the cost function be discounted. In this paper, we modify OP to overcome these limitations, and we call the new algorithm OPmin. New near-optimality and performance guarantees for OPmin are derived, which have major advantages compared to those originally given for OP. We also prove that a system whose inputs are generated by OPmin in a receding-horizon fashion exhibits stability properties. As a result, OPmin provides a new tool for the near-optimal, stable control of nonlinear switched discrete-time systems for generic cost functions.
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    Finite-horizon discounted optimal control: stability and performance
    Granzotto, M ; Postoyan, R ; Busoniu, L ; Nesic, D ; Daafouz, J (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    Motivated by (approximate) dynamic programming and model predictive control problems, we analyse the stability of deterministic nonlinear discrete-time systems whose inputs minimize a discounted finite-horizon cost. We assume that the system satisfies stabilizability and detectability properties with respect to the stage cost. Then, a Lyapunov function for the closed-loop system is constructed and a uniform semiglobal stability property is ensured, where the adjustable parameters are both the discount factor and the horizon length, which corresponds to the number of iterations for dynamic programming algorithms like value iteration. Stronger stability properties such as global exponential stability are also provided by strengthening the initial assumptions. We give bounds on the discount factor and the horizon length under which stability holds. In addition, we provide new relationships between the optimal value functions of the discounted, undiscounted, infinite-horizon and finite-horizon costs respectively, which appear to be very different from those available in the literature.
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    Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Subgradient Methods
    Lin, Y ; Shames, I ; Nesic, D (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-09)
    In this article, we study the problem of minimizing the sum of potentially nondifferentiable convex cost functions with partially overlapping dependences in an asynchronous manner, where communication in the network is not coordinated. We study the behavior of an asynchronous algorithm based on dual decomposition and block coordinate subgradient methods under assumptions weaker than those used in the literature. At the same time, we allow different agents to use local stepsizes with no global coordination. Sufficient conditions are provided for almost sure convergence to the solution of the optimization problem. Under additional assumptions, we establish a sublinear convergence rate that, in turn, can be strengthened to the linear convergence rate if the problem is strongly convex and has Lipschitz gradients. We also extend available results in the literature by allowing multiple and potentially overlapping blocks to be updated at the same time with nonuniform and potentially time-varying probabilities assigned to different blocks. A numerical example is provided to illustrate the effectiveness of the algorithm.