Electrical and Electronic Engineering - Research Publications

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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, 2021-09-01)
    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.
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    Security metrics and synthesis of secure control systems
    Murguia, C ; Shames, I ; Ruths, J ; Nešić, D (Elsevier Inc., 2020-05-01)
    The term stealthy has come to encompass a variety of techniques that attackers can employ to avoid being detected. In this manuscript, for a class of perturbed linear time-invariant systems, we propose two security metrics to quantify the potential impact that stealthy attacks could have on the system dynamics by tampering with sensor measurements. We provide analysis tools to quantify these metrics for given system dynamics, control, and system monitor. Then, we provide synthesis tools (in terms of semidefinite programs) to redesign controllers and monitors such that the impact of stealthy attacks is minimized and the required attack-free system performance is guaranteed.
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    Zeroth-Order Optimization on Subsets of Symmetric Matrices With Application to MPC Tuning
    Maass, A ; Manzie, C ; Shames, I ; Nakada, H (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-11-08)
    This article provides a zeroth-order optimization framework for nonsmooth and possibly nonconvex cost functions with matrix parameters that are real and symmetric. We provide complexity bounds on the number of iterations required to ensure a given accuracy level for both the convex and nonconvex cases. The derived complexity bounds for the convex case are less conservative than available bounds in the literature since we exploit the symmetric structure of the underlying matrix space. Moreover, the nonconvex complexity bounds are novel for the class of optimization problems that we consider. The utility of the framework is evident in the suite of applications that use symmetric matrices as tuning parameters. Of primary interest here is the challenge of tuning the gain matrices in model predictive controllers, as this is a challenge known to be inhibiting the industrial implementation of these architectures. To demonstrate the framework, we consider the problem of MIMO diesel air-path control and implement the framework iteratively ``in-the-loop'' to reduce tracking error on the output channels. Both simulations and experimental results are included to illustrate the effectiveness of the proposed framework over different engine drive cycles.
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    Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints
    Renganathan, V ; Shames, I ; Summers, TH (ELSEVIER, 2020-01-01)
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    Distributed Signal Signature Minimization Via Network Topology Modification
    Zamani, M ; Shames, I ; Hunjet, R (ELSEVIER, 2020-01-01)
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    Exploiting Structure in the Bottleneck Assignment Problem
    Khoo, M ; Wood, TA ; Manzie, C ; Shames, I (ELSEVIER, 2020-01-01)
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    Privacy Against State Estimation: An Optimization Framework based on the Data Processing Inequality
    Murguia, C ; Shames, I ; Farokhi, F ; Nesic, D (ELSEVIER, 2020-01-01)
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    On Privacy of Dynamical Systems: An Optimal Probabilistic Mapping Approach
    Murguia, C ; Shames, I ; Farokhi, F ; Nesic, D ; Poor, HV (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-01-01)
    We address the problem of maximizing privacy of stochastic dynamical systems whose state information is released through quantized sensor data. In particular, we consider the setting where information about the system state is obtained using noisy sensor measurements. This data is quantized and transmitted to a (possibly untrustworthy) remote station through a public/unsecured communication network. We aim at keeping (part of) the state of the system private; however, because the network (and/or the remote station) might be unsecure, adversaries might have access to sensor data, which can be used to estimate the system state. To prevent such adversaries from obtaining an accurate state estimate, before transmission, we randomize quantized sensor data using additive random vectors, and send the corrupted data to the remote station instead. We design the joint probability distribution of these additive vectors (over a time window) to minimize the mutual information (our privacy metric) between some linear function of the system state (a desired private output) and the randomized sensor data for a desired level of distortion-how different quantized sensor measurements and distorted data are allowed to be. We pose the problem of synthesising the joint probability distribution of the additive vectors as a convex program subject to linear constraints. Simulation experiments are presented to illustrate our privacy scheme.
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    Rigid-Profile Input Scheduling Under Constrained Dynamics With a Water Network Application
    Lang, A ; Cantoni, M ; Farokhi, F ; Shames, I (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-11-01)
    The motivation for this work stems from the problem of scheduling requests for flow at supply points located throughout an automated network of open-water channels. The off-take flows are rigid-profile inputs to the system dynamics. In particular, the channel operator can only shift orders in time to satisfy constraints on the automatic response to changes in the load. This leads to a nonconvex semi-infinite programming problem, with a sum-separable cost that encodes the collective sensitivity of end-users to scheduling delays. The constraints encode the linear time-invariant continuous-time dynamics and limits on the state across a continuous scheduling horizon. Discretization is used to arrive at a more manageable approximation of the semi-infinite program. A method for parsimoniously refining the discretization is applied to ensure continuous-time feasibility for solutions of the approximate problem. It is then shown how to improve the cost without loss of feasibility. Supporting analysis is provided, along with simulation results for a realistic irrigation channel setup to illustrate the approach.
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    Structured computation of optimal controls for constrained cascade systems
    Cantoni, M ; Farokhi, F ; Kerrigan, E ; Shames, I (Taylor & Francis, 2020-01)
    Constrained finite-horizon linear-quadratic optimal control problems are studied within the context of discrete-time dynamics that arise from the series interconnection of subsystems. A structured algorithm is devised for computing the Newton-like steps of primal-dual interior-point methods for solving a particular re-formulation of the problem as a quadratic program. This algorithm has the following properties: (i) the computation cost scales linearly in the number of subsystems along the cascade; and (ii) the computations can be distributed across a linear processor network, with localised problem data dependencies between the processor nodes and low communication overhead. The computation cost of the approach, which is based on a fixed permutation of the primal and dual variables, scales cubically in the time horizon of the original optimal control problem. Limitations in these terms are explored as part of a numerical example. This example involves application of the main results to model data for the cascade dynamics of an automated irrigation channel in particular.