494 results on '"Nesic, Dragan"'
Search Results
2. Hybrid low-dimensional limiting state of charge estimator for multi-cell lithium-ion batteries
- Author
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Khalil, Mira, Postoyan, Romain, Raël, Stéphane, and Nešić, Dragan
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Electrical Engineering and Systems Science - Systems and Control - Abstract
The state of charge (SOC) of lithium-ion batteries needs to be accurately estimated for safety and reliability purposes. For battery packs made of a large number of cells, it is not always feasible to design one SOC estimator per cell due to limited computational resources. Instead, only the minimum and the maximum SOC need to be estimated. The challenge is that the cells having minimum and maximum SOC typically change over time. In this context, we present a low-dimensional hybrid estimator of the minimum (maximum) SOC, whose convergence is analytically guaranteed. We consider for this purpose a battery consisting of cells interconnected in series, which we model by electric equivalent circuit models. We then present the hybrid estimator, which runs an observer designed for a single cell at any time instant, selected by a switching-like logic mechanism. We establish a practical exponential stability property for the estimation error on the minimum (maximum) SOC thereby guaranteeing the ability of the hybrid scheme to generate accurate estimates of the minimum (maximum) SOC. The analysis relies on non-smooth hybrid Lyapunov techniques. A numerical illustration is provided to showcase the relevance of the proposed approach.
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- 2024
3. Emulation-based Stabilization for Networked Control Systems with Stochastic Channels
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Ren, Wei, Wang, Wei, Pan, Zhuo-Rui, Sun, Xi-Ming, Teel, Andrew R., and Nesic, Dragan
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Electrical Engineering and Systems Science - Systems and Control - Abstract
This paper studies the stabilization problem of networked control systems (NCSs) with random packet dropouts caused by stochastic channels. To describe the effects of stochastic channels on the information transmission, the transmission times are assumed to be deterministic, whereas the packet transmission is assumed to be random. We first propose a stochastic scheduling protocol to model random packet dropouts, and address the properties of the proposed stochastic scheduling protocol. The proposed scheduling protocol provides a unified modelling framework for a general class of random packet dropouts due to different stochastic channels. Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts. Based on this stochastic hybrid model, we follow the emulation approach to establish sufficient conditions to guarantee uniform global asymptotical stability in probability. In particular, an upper bound on the maximally allowable transmission interval is derived explicitly for all stochastic protocols satisfying Lyapunov conditions that guarantee uniform global asymptotic stability in probability. Finally, two numerical examples are presented to demonstrate the derived results., Comment: 12 pages, 4 figures, accepted
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- 2024
4. Attack-Resilient Design for Connected and Automated Vehicles
- Author
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Yang, Tianci, Murguia, Carlos, Nesic, Dragan, and Yuen, Chau
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Electrical Engineering and Systems Science - Systems and Control - Abstract
By sharing local sensor information via Vehicle-to-Vehicle (V2V) wireless communication networks, Cooperative Adaptive Cruise Control (CACC) is a technology that enables Connected and Automated Vehicles (CAVs) to drive autonomously on the highway in closely-coupled platoons. The use of CACC technologies increases safety and the traffic throughput, and decreases fuel consumption and CO2 emissions. However, CAVs heavily rely on embedded software, hardware, and communication networks that make them vulnerable to a range of cyberattacks. Cyberattacks to a particular CAV compromise the entire platoon as CACC schemes propagate corrupted data to neighboring vehicles potentially leading to traffic delays and collisions. Physics-based monitors can be used to detect the presence of False Data Injection (FDI) attacks to CAV sensors; however, unavoidable system disturbances and modelling uncertainty often translates to conservative detection results. Given enough system knowledge, adversaries are still able to launch a range of attacks that can surpass the detection scheme by hiding within the system disturbances and uncertainty -- we refer to this class of attacks as \textit{stealthy FDI attacks}. Stealthy attacks are hard to deal with as they affect the platoon dynamics without being noticed. In this manuscript, we propose a co-design methodology (built around a series convex programs) to synthesize distributed attack monitors and $H_{\infty}$ CACC controllers that minimize the joint effect of stealthy FDI attacks and system disturbances on the platoon dynamics while guaranteeing a prescribed platooning performance (in terms of tracking and string stability). Computer simulations are provided to illustrate the performance of out tools., Comment: arXiv admin note: text overlap with arXiv:2109.01553
- Published
- 2023
5. Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints
- Author
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Siriya, Seth, Zhu, Jingge, Nešić, Dragan, and Pu, Ye
- Subjects
Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Machine Learning ,Mathematics - Optimization and Control - Abstract
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown. To address this challenge, we propose a certainty-equivalent control scheme which combines online parameter estimation with saturated linear control. We establish the existence of a high probability stability bound on the closed-loop system, under additional assumptions on the system and noise processes. Finally, numerical examples are presented to illustrate our results., Comment: 21 pages, 1 figure, submitted to 62nd IEEE Conference on Decision and Control
- Published
- 2023
6. Transmit power policies for stochastic stabilisation of multi-link wireless networked control systems
- Author
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Maass, Alejandro I., Nesic, Dragan, Postoyan, Romain, Varma, Vineeth S., and Lasaulce, Samson
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Electrical Engineering and Systems Science - Systems and Control - Abstract
Transmit power control is one of the most important issues in wireless networks, where nodes typically operate on limited battery power. Reducing communicating power consumption is essential for both economic and ecologic reasons. In fact, transmitting at unnecessarily high power not only reduces node lifetime, but also introduces excessive interference and electromagnetic pollution. Existing work in the wireless community mostly focus on designing transmit power policies by taking into account communication aspects like quality of service or network capacity. Wireless networked control systems (WNCSs), on the other hand, have different and specific needs such as stability, which require transmit power policies adapted to the control context. Transmit power design in the control community has recently attracted much attention, and available works mostly consider linear systems or specific classes of non-linear systems with a single-link view of the system. In this paper, we propose a framework for the design of stabilising transmit power levels that applies to much larger classes of non-linear plants, controllers, and multi-link setting. By exploiting the fact that channel success probabilities are related to transmit power in a non-linear fashion, we first derive closed-loop stability conditions that relate channel probabilities with transmission rate. Next, we combine these results together with well-known and realistic interference models to provide a design methodology for stabilising transmit power in non-linear and multi-link WNCSs., Comment: 18 pages, 3 figures
- Published
- 2022
7. Policy iteration: for want of recursive feasibility, all is not lost
- Author
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Granzotto, Mathieu, De Silva, Olivier Lindamulage, Postoyan, Romain, Nesic, Dragan, and Jiang, Zhong-Ping
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control - Abstract
This paper investigates recursive feasibility, recursive robust stability and near-optimality properties of policy iteration (PI). For this purpose, we consider deterministic nonlinear discrete-time systems whose inputs are generated by PI for undiscounted cost functions. We first assume that PI is recursively feasible, in the sense that the optimization problems solved at each iteration admit a solution. In this case, we provide novel conditions to establish recursive robust stability properties for a general attractor, meaning that the policies generated at each iteration ensure a robust \KL-stability property with respect to a general state measure. We then derive novel explicit bounds on the mismatch between the (suboptimal) value function returned by PI at each iteration and the optimal one. Afterwards, motivated by a counter-example that shows that PI may fail to be recursively feasible, we modify PI so that recursive feasibility is guaranteed a priori under mild conditions. This modified algorithm, called PI+, is shown to preserve the recursive robust stability when the attractor is compact. Additionally, PI+ enjoys the same near-optimality properties as its PI counterpart under the same assumptions. Therefore, PI+ is an attractive tool for generating near-optimal stabilizing control of deterministic discrete-time nonlinear systems., Comment: Submitted for review
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- 2022
8. Stability analysis of optimal control problems with time-dependent costs
- Author
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Benahmed, Sifeddine, Postoyan, Romain, Granzotto, Mathieu, Buşoniu, Lucian, Daafouz, Jamal, and Nešić, Dragan
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Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
We present stability conditions for deterministic time-varying nonlinear discrete-time systems whose inputs aim to minimize an infinite-horizon time-dependent cost. Global asymptotic and exponential stability properties for general attractors are established. This work covers and generalizes the related results on discounted optimal control problems to more general systems and cost functions.
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- 2022
9. Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints
- Author
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Siriya, Seth, Zhu, Jingge, Nešić, Dragan, and Pu, Ye
- Subjects
Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Machine Learning ,Mathematics - Optimization and Control - Abstract
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results., Comment: 16 pages, 2 figures, accepted at IEEE Control Systems Letters
- Published
- 2022
10. Energy-efficient transmission policies for the linear quadratic control of scalar systems
- Author
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Sun, Yifei, Lasaulce, Samson, Kieffer, Michel, Postoyan, Romain, and Nešić, Dragan
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Electrical Engineering and Systems Science - Systems and Control - Abstract
This paper considers controlled scalar systems relying on a lossy wireless feedback channel. In contrast with the existing literature, the focus is not on the system controller but on the wireless transmit power controller that is implemented at the system side for reporting the state to the controller. Such a problem may be of interest, \emph{e.g.}, for the remote control of drones, where communication costs may have to be considered. Determining the power control policy that minimizes the combination of the dynamical system cost and the wireless transmission energy is shown to be a non-trivial optimization problem. It turns out that the recursive structure of the problem can be exploited to determine the optimal power control policy. As illustrated in the numerical performance analysis, in the scenario of a dynamics without perturbations, the optimal power control policy consists in decreasing the transmit power at the right pace. This allows a significant performance gain compared to conventional policies such as the full transmit power policy or the open-loop policy.
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- 2022
11. Online Convex Optimization Using Coordinate Descent Algorithms
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Lin, Yankai, Shames, Iman, and Nešić, Dragan
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control ,68Q32 (Primary), 68T05, 90C25 (Secondary) - Abstract
This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results., Comment: Accepted for publication in Automatica
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- 2022
- Full Text
- View/download PDF
12. Online convex optimization using coordinate descent algorithms
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Lin, Yankai, Shames, Iman, and Nešić, Dragan
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- 2024
- Full Text
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13. Exploiting homogeneity for the optimal control of discrete-time systems: application to value iteration
- Author
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Granzotto, Mathieu, Postoyan, Romain, Buşoniu, Lucian, Nešić, Dragan, and Daafouz, Jamal
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Mathematics - Optimization and Control - Abstract
To investigate solutions of (near-)optimal control problems, we extend and exploit a notion of homogeneity recently proposed in the literature for discrete-time systems. Assuming the plant dynamics is homogeneous, we first derive a scaling property of its solutions along rays provided the sequence of inputs is suitably modified. We then consider homogeneous cost functions and reveal how the optimal value function scales along rays. This result can be used to construct (near-)optimal inputs on the whole state space by only solving the original problem on a given compact manifold of a smaller dimension. Compared to the related works of the literature, we impose no conditions on the homogeneity degrees. We demonstrate the strength of this new result by presenting a new approximate scheme for value iteration, which is one of the pillars of dynamic programming. The new algorithm provides guaranteed lower and upper estimates of the true value function at any iteration and has several appealing features in terms of reduced computation. A numerical case study is provided to illustrate the proposed algorithm., Comment: Long version (with proofs) of CDC 2021 paper
- Published
- 2021
14. A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks
- Author
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Yang, Tianci, Murguia, Carlos, Nešić, Dragan, and Lv, Chen
- Subjects
Electrical Engineering and Systems Science - Systems and Control - Abstract
Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{\infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach., Comment: arXiv admin note: text overlap with arXiv:2103.00883
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- 2021
15. Ordinal Optimisation and the Offline Multiple Noisy Secretary Problem
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Chin, Robert, Rowe, Jonathan E., Shames, Iman, Manzie, Chris, and Nešić, Dragan
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Mathematics - Optimization and Control - Abstract
We study the success probability for a variant of the secretary problem, with noisy observations and multiple offline selection. Our formulation emulates, and is motivated by, problems involving noisy selection arising in the disciplines of stochastic simulation and simulation-based optimisation. In addition, we employ the philosophy of ordinal optimisation - involving an ordinal selection rule, and a percentile notion of goal softening for the success probability. As a result, it is shown that the success probability only depends on the underlying copula of the problem. Other general properties for the success probability are also presented. Specialising to the case of Gaussian copulas, we also derive an analytic lower bound for the success probability, which may then be inverted to find sufficiently large sample sizes that guarantee a high success probability arbitrarily close to one., Comment: 10 pages plus 9 pages of appendices
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- 2021
16. Decentralized event-triggered estimation of nonlinear systems
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Petri, Elena, Postoyan, Romain, Astolfi, Daniele, Nešić, Dragan, and Heemels, W.P.M.H. (Maurice)
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- 2024
- Full Text
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17. On Joint Reconstruction of State and Input-Output Injection Attacks for Nonlinear Systems
- Author
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Yang, Tianci, Murguia, Carlos, Lv, Chen, Nesic, Dragan, and Huang, Chao
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Electrical Engineering and Systems Science - Systems and Control - Abstract
We address the problem of robust state reconstruction for discrete-time nonlinear systems when the actuators and sensors are injected with (potentially unbounded) attack signals. Exploiting redundancy in sensors and actuators and using a bank of unknown input observers (UIOs), we propose an observer-based estimator capable of providing asymptotic estimates of the system state and attack signals under the condition that the numbers of sensors and actuators under attack are sufficiently small. Using the proposed estimator, we provide methods for isolating the compromised actuators and sensors. Numerical examples are provided to demonstrate the effectiveness of our methods., Comment: arXiv admin note: text overlap with arXiv:1904.04237
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- 2021
18. A Sequential Learning Algorithm for Probabilistically Robust Controller Tuning
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Chin, Robert, Manzie, Chris, Shames, Iman, Nešić, Dragan, and Rowe, Jonathan E.
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control - Abstract
We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the internal performance constraint to a chance-constrained program which has black-box functions. The algorithm leverages ideas from the areas of randomised algorithms and ordinal optimisation, and also draws comparisons with the scenario approach; these have all been previously applied to finding approximate solutions for difficult design problems. By exploiting statistical correlations through black-box sampling, we formally prove that our algorithm yields a controller meeting the prescribed probabilistic performance specification. Additionally, we characterise the computational requirement of the algorithm with a probabilistic lower bound on the algorithm's stopping time. To validate our work, the algorithm is then demonstrated for tuning model predictive controllers on a diesel engine air-path across a fleet of vehicles. The algorithm successfully tuned a single controller to meet a desired tracking error performance, even in the presence of the plant uncertainty inherent across the fleet. Moreover, the algorithm was shown to exhibit a sample complexity comparable to the scenario approach., Comment: 17 pages including appendices and references
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- 2021
19. Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Subgradient Methods
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Lin, Yankai, Shames, Iman, and Nesic, Dragan
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control ,93D99 (primary), 90C25 (secondary), 49M29 - Abstract
We study the problem of minimizing the sum of potentially non-differentiable 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 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 non-uniform 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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- 2021
20. When to stop value iteration: stability and near-optimality versus computation
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Granzotto, Mathieu, Postoyan, Romain, Nešić, Dragan, Buşoniu, Lucian, and Daafouz, Jamal
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Mathematics - Optimization and Control - Abstract
Value iteration (VI) is a ubiquitous algorithm for optimal control, planning, and reinforcement learning schemes. Under the right assumptions, VI is a vital tool to generate inputs with desirable properties for the controlled system, like optimality and Lyapunov stability. As VI usually requires an infinite number of iterations to solve general nonlinear optimal control problems, a key question is when to terminate the algorithm to produce a "good" solution, with a measurable impact on optimality and stability guarantees. By carefully analysing VI under general stabilizability and detectability properties, we provide explicit and novel relationships of the stopping criterion's impact on near-optimality, stability and performance, thus allowing to tune these desirable properties against the induced computational cost. The considered class of stopping criteria encompasses those encountered in the control, dynamic programming and reinforcement learning literature and it allows considering new ones, which may be useful to further reduce the computational cost while endowing and satisfying stability and near-optimality properties. We therefore lay a foundation to endow machine learning schemes based on VI with stability and performance guarantees, while reducing computational complexity., Comment: Submitted for 3rd L4DC
- Published
- 2020
21. Tracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation
- Author
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Maass, Alejandro I., Manzie, Chris, Nesic, Dragan, Manton, Jonathan H., and Shames, Iman
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Mathematics - Optimization and Control ,68T05, 68Q32 (Primary), 90C25, 90C56 (Secondary) - Abstract
We study numerical optimisation algorithms that use zeroth-order information to minimise time-varying geodesically-convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant cases. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalisation of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimise the algorithm's performance. Our results illustrate how the manifold geometry in terms of the sectional curvature affects these bounds. Additionally, we provide dynamic regret bounds for this online optimisation setting. To the best of our knowledge, these are the first regret bounds even for the Euclidean version of the problem. Lastly, via numerical simulations, we demonstrate the applicability of our algorithm on an online Karcher mean problem., Comment: 27 pages, 2 figures
- Published
- 2020
22. On the Latency, Rate and Reliability Tradeoff in Wireless Networked Control Systems for IIoT
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Liu, Wanchun, Nair, Girish, Li, Yonghui, Nesic, Dragan, Vucetic, Branka, and Poor, H. Vincent
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Computer Science - Information Theory ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Wireless networked control systems (WNCSs) provide a key enabling technique for Industry Internet of Things (IIoT). However, in the literature of WNCSs, most of the research focuses on the control perspective, and has considered oversimplified models of wireless communications which do not capture the key parameters of a practical wireless communication system, such as latency, data rate and reliability. In this paper, we focus on a WNCS, where a controller transmits quantized and encoded control codewords to a remote actuator through a wireless channel, and adopt a detailed model of the wireless communication system, which jointly considers the inter-related communication parameters. We derive the stability region of the WNCS. If and only if the tuple of the communication parameters lies in the region, the average cost function, i.e., a performance metric of the WNCS, is bounded. We further obtain a necessary and sufficient condition under which the stability region is $n$-bounded, where $n$ is the control codeword blocklength. We also analyze the average cost function of the WNCS. Such analysis is non-trivial because the finite-bit control-signal quantizer introduces a non-linear and discontinuous quantization function which makes the performance analysis very difficult. We derive tight upper and lower bounds on the average cost function in terms of latency, data rate and reliability. Our analytical results provide important insights into the design of the optimal parameters to minimize the average cost within the stability region., Comment: Paper accepted by IEEE Internet of Things Journal. Copyright may be transferred without notice, after which this version may no longer be accessible
- Published
- 2020
23. Active Learning for Linear Parameter-Varying System Identification
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Chin, Robert, Maass, Alejandro I., Ulapane, Nalika, Manzie, Chris, Shames, Iman, Nešić, Dragan, Rowe, Jonathan E., and Nakada, Hayato
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Electrical Engineering and Systems Science - Systems and Control - Abstract
Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework., Comment: 6 pages
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- 2020
24. Tuning of multivariable model predictive controllersthrough expert bandit feedback
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Ira, Alex. S., Manzie, Chris, Shames, Iman, Chin, Robert, Nesic, Dragan, Nakada, Hayato, and Sano, Takeshi
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Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control ,93C83, 93C85, 93C95, 90C56, 90C90 - Abstract
For certain industrial control applications an explicit function capturing the nontrivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimization algorithm that can efficiently deal with a large number of variables, is used for optimizing the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.
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- 2020
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25. Ordinal Optimisation for the Gaussian Copula Model
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Chin, Robert, Rowe, Jonathan E., Shames, Iman, Manzie, Chris, and Nešić, Dragan
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Mathematics - Optimization and Control ,Mathematics - Probability - Abstract
We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of $\mathbb{R}^{d}$). Our formulation invokes an assumption of a Gaussian copula model, and we show that the success probability can be equivalently computed by assuming a special case of additive noise. We formally prove a lower bound on the success probability under the Gaussian copula model, and numerical experiments demonstrate that the lower bound yields a reasonable approximation to the actual success probability. Lastly, we showcase the utility of our results by guaranteeing high success probabilities with ordinal optimisation., Comment: 18 pages, including appendices and references
- Published
- 2019
26. Optimistic planning for the near-optimal control of nonlinear switched discrete-time systems with stability guarantees
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Granzotto, Mathieu, Postoyan, Romain, Buşoniu, Lucian, Nešić, Dragan, and Daafouz, Jamal
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control - Abstract
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. However, OP exhibits several limitations, which prevent its application in a standard control context. First, it requires the stage cost to take values in [0,1], an unnatural prerequisite as it excludes, for instance, quadratic stage costs. Second, it requires the cost function to be discounted. Third, it applies for reward maximization, and not cost minimization. In this paper, we modify OP to overcome these limitations, and we call the new algorithm OPmin. We then make stabilizability and detectability assumptions, under which we derive near-optimality guarantees for OPmin and we show that the obtained bound has major advantages compared to the bound originally given by OP. In addition, we 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., Comment: 8 pages, 2019 conference in decision and control, longer version submitted for reviewers
- Published
- 2019
27. Information-Theoretic Privacy through Chaos Synchronization and Optimal Additive Noise
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Murguia, Carlos, Shames, Iman, Farokhi, Farhad, and Nesic, Dragan
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Electrical Engineering and Systems Science - Systems and Control - Abstract
We study the problem of maximizing privacy of data sets by adding random vectors generated via synchronized chaotic oscillators. In particular, we consider the setup where information about data sets, queries, is sent through public (unsecured) communication channels to a remote station. To hide private features (specific entries) within the data set, we corrupt the response to queries by adding random vectors. We send the distorted query (the sum of the requested query and the random vector) through the public channel. The distribution of the additive random vector is designed to minimize the mutual information (our privacy metric) between private entries of the data set and the distorted query. We cast the synthesis of this distribution as a convex program in the probabilities of the additive random vector. Once we have the optimal distribution, we propose an algorithm to generate pseudo-random realizations from this distribution using trajectories of a chaotic oscillator. At the other end of the channel, we have a second chaotic oscillator, which we use to generate realizations from the same distribution. Note that if we obtain the same realizations on both sides of the channel, we can simply subtract the realization from the distorted query to recover the requested query. To generate equal realizations, we need the two chaotic oscillators to be synchronized, i.e., we need them to generate exactly the same trajectories on both sides of the channel synchronously in time. We force the two chaotic oscillators into exponential synchronization using a driving signal. Simulations are presented to illustrate our results., Comment: arXiv admin note: text overlap with arXiv:1809.03133 by other authors
- Published
- 2019
28. A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks
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Yang, Tianci, Murguia, Carlos, Kuijper, Margreta, and Nesic, Dragan
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Computer Science - Systems and Control - Abstract
We address the problem of state estimation and attack isolation for general discrete-time nonlinear systems when sensors are corrupted by (potentially unbounded) attack signals. For a large class of nonlinear plants and observers, we provide a general estimation scheme, built around the idea of sensor redundancy and multi-observer, capable of reconstructing the system state in spite of sensor attacks and noise. This scheme has been proposed by others for linear systems/observers and here we propose a unifying framework for a much larger class of nonlinear systems/observers. Using the proposed estimator, we provide an isolation algorithm to pinpoint attacks on sensors during sliding time windows. Simulation results are presented to illustrate the performance of our tools., Comment: arXiv admin note: text overlap with arXiv:1806.06484
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- 2019
29. An Unknown Input Multi-Observer Approach for Estimation and Control under Adversarial Attacks
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Yang, Tianci, Murguia, Carlos, Kuijper, Margreta, and Nesic, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
We address the problem of state estimation, attack isolation, and control of discrete-time linear time-invariant systems under (potentially unbounded) actuator and sensor false data injection attacks. Using a bank of unknown input observers, each observer leading to an exponentially stable estimation error (in the attack-free case), we propose an observer-based estimator that provides exponential estimates of the system state in spite of actuator and sensor attacks. Exploiting sensor and actuator redundancy, the estimation scheme is guaranteed to work if a sufficiently small subset of sensors and actuators are under attack. Using the proposed estimator, we provide tools for reconstructing and isolating actuator and sensor attacks; and a control scheme capable of stabilizing the closed-loop dynamics by switching off isolated actuators. Simulation results are presented to illustrate the performance of our tools., Comment: arXiv admin note: substantial text overlap with arXiv:1811.10159
- Published
- 2019
30. Adaptive Scan for Atomic Force Microscopy Based on Online Optimisation: Theory and Experiment
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Wang, Kaixiang, Ruppert, Michael G., Manzie, Chris, Nesic, Dragan, and Yong, Yuen K.
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
A major challenge in Atomic Force Microscopy (AFM) is to reduce the scan duration while retaining the image quality. Conventionally, the scan rate is restricted to a sufficiently small value in order to ensure a desirable image quality as well as a safe tip-sample contact force. This usually results in a conservative scan rate for samples that have a large variation in aspect ratio and/or for scan patterns that have a varying linear velocity. In this paper, an adaptive scan scheme is proposed to alleviate this problem. A scan line-based performance metric balancing both imaging speed and accuracy is proposed, and the scan rate is adapted such that the metric is optimised online in the presence of aspect ratio and/or linear velocity variations. The online optimisation is achieved using an extremum-seeking (ES) approach, and a semi-global practical asymptotic stability (SGPAS) result is shown for the overall system. Finally, the proposed scheme is demonstrated via both simulation and experiment.
- Published
- 2019
31. An Unknown Input Multi-Observer Approach for Estimation, Attack Isolation, and Control of LTI Systems under Actuator Attacks
- Author
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Yang, Tianci, Murguia, Carlos, Kuijper, Margreta, and Nesic, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
We address the problem of state estimation, attack isolation, and control for discrete-time Linear Time Invariant (LTI) systems under (potentially unbounded) actuator false data injection attacks. Using a bank of Unknown Input Observers (UIOs), each observer leading to an exponentially stable estimation error in the attack-free case, we propose an estimator that provides exponential estimates of the system state and the attack signals when a sufficiently small number of actuators are attacked. We use these estimates to control the system and isolate actuator attacks. Simulations results are presented to illustrate the performance of the results.
- Published
- 2018
32. Closeness of Solutions for Singularly Perturbed Systems via Averaging
- Author
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Deghat, Mohammad, Ahmadizadeh, Saeed, Nesic, Dragan, and Manzie, Chris
- Subjects
Computer Science - Systems and Control - Abstract
This paper studies the behavior of singularly perturbed nonlinear differential equations with boundary-layer solutions that do not necessarily converge to an equilibrium. Using the average of the fast variable and assuming the boundary layer solutions converge to a bounded set, results on the closeness of solutions of the singularly perturbed system to the solutions of the reduced average and boundary layer systems over a finite time interval are presented. The closeness of solutions error is shown to be of order O(\sqrt(\epsilon)), where \epsilon is the perturbation parameter.
- Published
- 2018
33. On Privacy of Quantized Sensor Measurements through Additive Noise
- Author
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Murguia, Carlos, Shames, Iman, Farokhi, Farhad, and Nesic, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion -- how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.
- Published
- 2018
34. Security Metrics of Networked Control Systems under Sensor Attacks (extended preprint)
- Author
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Murguia, Carlos, Shames, Iman, Ruths, Justin, and Nesic, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
As more attention is paid to security in the context of control systems and as attacks occur to real control systems throughout the world, it has become clear that some of the most nefarious attacks are those that evade detection. 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 mathematical tools (in terms of linear matrix inequalities) to quantify these metrics for given system dynamics, control structure, system monitor, and set of sensors being attacked. 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.
- Published
- 2018
35. A Multi-Observer Approach for Attack Detection and Isolation of Discrete-Time Nonlinear Systems
- Author
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Yang, Tianci, Murguia, Carlos, Kuijper, Margreta, and Nešić, Dragan
- Subjects
Computer Science - Systems and Control ,Computer Science - Cryptography and Security - Abstract
We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are `consistent' with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms., Comment: arXiv admin note: text overlap with arXiv:1805.04242
- Published
- 2018
36. A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks and Measurement Noise
- Author
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Yang, Tianci, Murguia, Carlos, Kuijper, Margreta, and Nešić, Dragan
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Systems and Control - Abstract
We address the problem of robust state estimation of a class of discrete-time nonlinear systems with positive-slope nonlinearities when the sensors are corrupted by (potentially unbounded) attack signals and bounded measurement noise. We propose an observer-based estimator, using a bank of circle-criterion observers, which provides a robust estimate of the system state in spite of sensor attacks and measurement noise. We first consider the attack-free case where there is measurement noise and we provide a design method for a robust circle-criterion observer. Then, we consider the case when a sufficiently small subset of sensors are subject to attacks and all sensors are affected by measurement noise. We use our robust circle-criterion observer as the main ingredient in building an estimator that provides robust state estimation in this case. Finally, we propose an algorithm for isolating attacked sensors in the case of bounded measurement noise. We test this algorithm through simulations., Comment: arXiv admin note: text overlap with arXiv:1806.06484
- Published
- 2018
37. Sampled-data extremum-seeking framework for constrained optimization of nonlinear dynamical systems
- Author
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Hazeleger, Leroy, Nešić, Dragan, and van de Wouw, Nathan
- Published
- 2022
- Full Text
- View/download PDF
38. Nonlinear Sampled-Data Systems
- Author
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Nesic, Dragan, Postoyan, Romain, Baillieul, John, editor, and Samad, Tariq, editor
- Published
- 2021
- Full Text
- View/download PDF
39. Secure Networked Control Systems Design Using Semi-homomorphic Encryption
- Author
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Lin, Yankai, Farokhi, Farhad, Shames, Iman, Nešić, Dragan, Allgöwer, Frank, Series Editor, Morari, Manfred, Series Editor, Fleming, P., Advisory Editor, Kokotovic, P., Advisory Editor, Kurzhanski, A. B., Advisory Editor, Kwakernaak, H., Advisory Editor, Rantzer, A., Advisory Editor, Tsitsiklis, J. N., Advisory Editor, Ferrari, Riccardo M.G., editor, and Teixeira, André M. H., editor
- Published
- 2021
- Full Text
- View/download PDF
40. Supervisory observer for parameter and state estimation of nonlinear systems using the DIRECT algorithm
- Author
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Chong, Michelle S., Postoyan, Romain, Khong, Sei Zhen, and Nesic, Dragan
- Subjects
Mathematics - Optimization and Control - Abstract
A supervisory observer is a multiple-model architecture, which estimates the parameters and the states of nonlinear systems. It consists of a bank of state observers, where each observer is designed for some nominal parameter values sampled in a known parameter set. A selection criterion is used to select a single observer at each time instant, which provides its state estimate and parameter value. The sampling of the parameter set plays a crucial role in this approach. Existing works require a sufficiently large number of parameter samples, but no explicit lower bound on this number is provided. The aim of this work is to overcome this limitation by sampling the parameter set automatically using an iterative global optimisation method, called DIviding RECTangles (DIRECT). Using this sampling policy, we start with 1 + 2np parameter samples where np is the dimension of the parameter set. Then, the algorithm iteratively adds samples to improve its estimation accuracy. Convergence guarantees are provided under the same assumptions as in previous works, which include a persistency of excitation condition. The efficacy of the supervisory observer with the DIRECT sampling policy is illustrated on a model of neural populations.
- Published
- 2017
41. On Eigenvalues of Laplacian Matrix for a Class of Directed Signed Graphs
- Author
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Ahmadizadeh, Saeed, Shames, Iman, Martin, Samuel, and Nesic, Dragan
- Subjects
Mathematics - Optimization and Control - Abstract
The eigenvalues of the Laplacian matrix for a class of directed graphs with both positive and negative weights are studied. First, a class of directed signed graphs is investigated in which one pair of nodes (either connected or not) is perturbed with negative weights. A necessary condition is proposed to attain the following objective for the perturbed graph: the real parts of the non-zero eigenvalues of its Laplacian matrix are positive. A sufficient condition is also presented that ensures the aforementioned objective for the unperturbed graph. It is then highlighted the case where the condition becomes necessary and sufficient. Secondly, for directed graphs, a subset of pairs of nodes are identified where if any of the pairs is connected by an edge with infinitesimal negative weight, the resulting Laplacian matrix will have at least one eigenvalue with negative real part. Illustrative examples are presented to show the applicability of our results.
- Published
- 2017
42. Information-Theoretic Privacy Through Chaos Synchronization and Optimal Additive Noise
- Author
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Murguia, Carlos, Shames, Iman, Farokhi, Farhad, Nešić, Dragan, and Farokhi, Farhad, editor
- Published
- 2020
- Full Text
- View/download PDF
43. Time scale modeling for consensus in sparse directed networks with time-varying topologies
- Author
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Martin, Samuel, Morarescu, Irinel-Constantin, and Nesic, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
The paper considers the consensus problem in large networks represented by time-varying directed graphs. A practical way of dealing with large-scale networks is to reduce their dimension by collapsing the states of nodes belonging to densely and intensively connected clusters into aggregate variables. It will be shown that under suitable conditions, the states of the agents in each cluster converge fast toward a local agreement. Local agreements correspond to aggregate variables which slowly converge to consensus. Existing results concerning the time-scale separation in large networks focus on fixed and undirected graphs. The aim of this work is to extend these results to the more general case of time-varying directed topologies. It is noteworthy that in the fixed and undirected graph case the average of the states in each cluster is time-invariant when neglecting the interactions between clusters. Therefore, they are good candidates for the aggregate variables. This is no longer possible here. Instead, we find suitable time-varying weights to compute the aggregate variables as time-invariant weighted averages of the states in each cluster. This allows to deal with the more challenging time-varying directed graph case. We end up with a singularly perturbed system which is analyzed by using the tools of two time-scales averaging which seem appropriate to this system.
- Published
- 2016
44. Optimization Methods on Riemannian Manifolds via Extremum Seeking Algorithms
- Author
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Taringoo, Farzin, Dower, Peter M., Nesic, Dragan, and Tan, Ying
- Subjects
Mathematics - Optimization and Control - Abstract
This paper formulates the problem of Extremum Seeking for optimization of cost functions defined on Riemannian manifolds. We extend the conventional extremum seeking algorithms for optimization problems in Euclidean spaces to optimization of cost functions defined on smooth Riemannian manifolds. This problem falls within the category of online optimization methods. We introduce the notion of geodesic dithers which is a perturbation of the optimizing trajectory in the tangent bundle of the ambient state manifolds and obtain the extremum seeking closed loop as a perturbation of the averaged gradient system. The main results are obtained by applying closeness of solutions and averaging theory on Riemannian manifolds. The main results are further extended for optimization on Lie groups. Numerical examples on Riemannian manifolds (Lie groups) SO(3) and SE(3) are presented at the end of the paper.
- Published
- 2014
45. Security metrics and synthesis of secure control systems
- Author
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Murguia, Carlos, Shames, Iman, Ruths, Justin, and Nešić, Dragan
- Published
- 2020
- Full Text
- View/download PDF
46. A Multi-observer Approach for Parameter and State Estimation of Nonlinear Systems with Slowly Varying Parameters
- Author
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Cuevas, Luis, Nešić, Dragan, Manzie, Chris, and Postoyan, Romain
- Published
- 2020
- Full Text
- View/download PDF
47. Privacy Against State estimation: An Optimization Framework based on the Data Processing Inequality
- Author
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Murguia, Carlos, Shames, Iman, Farokhi, Farhad, and Nešic, Dragan
- Published
- 2020
- Full Text
- View/download PDF
48. Tuning of model predictive engine controllers over transient drive cycles
- Author
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Maass, Alejandro I., Manzie, Chris, Shames, Iman, Chin, Robert, Nešić, Dragan, Ulapane, Nalika, and Nakada, Hayato
- Published
- 2020
- Full Text
- View/download PDF
49. Co-design of output feedback laws and event-triggering conditions for linear systems
- Author
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Abdelrahim, Mahmoud, Postoyan, Romain, Daafouz, Jamal, and Nešić, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
We present a procedure to simultaneously design the output feedback law and the event-triggering condition to stabilize linear systems. The closed-loop system is shown to satisfy a global asymptotic stability property and the existence of a strictly positive minimum amount of time between two transmissions is guaranteed. The event-triggered controller is obtained by solving linear matrix inequalities (LMIs). We then exploit the flexibility of the method to maximize the guaranteed minimum amount of time between two transmissions. Finally, we provide a (heuristic) method to reduce the amount of transmissions, which is supported by numerical simulations.
- Published
- 2014
50. Stabilization of nonlinear systems using event-triggered output feedback controllers
- Author
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Abdelrahim, Mahmoud, Postoyan, Romain, Daafouz, Jamal, and Nešić, Dragan
- Subjects
Computer Science - Systems and Control - Abstract
The objective is to design output feedback event-triggered controllers to stabilize a class of nonlinear systems. One of the main difficulties of the problem is to ensure the existence of a minimum amount of time between two consecutive transmissions, which is essential in practice. We solve this issue by combining techniques from event-triggered and time-triggered control. The idea is to turn on the event-triggering mechanism only after a fixed amount of time has elapsed since the last transmission. This time is computed based on results on the stabilization of time-driven sampled-data systems. The overall strategy ensures an asymptotic stability property for the closed-loop system. The results are proved to be applicable to linear time-invariant (LTI) systems as a particular case.
- Published
- 2014
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