| 1. |
- Alanwar, Amr, et al.
(author)
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Data-driven Set-based Estimation of Polynomial Systems with Application to SIR Epidemics
- 2022
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In: 2022 European Control Conference (ECC). - : IEEE. ; , s. 888-893
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Conference paper (peer-reviewed)abstract
- This paper proposes a data-driven set-based estimation algorithm for a class of nonlinear systems with polynomial nonlinearities. Using the system's input-output data, the proposed method computes a set that guarantees the inclusion of the system's state in real-time. Although the system is assumed to be a polynomial type, the exact polynomial functions, and their coefficients are assumed to be unknown. To this end, the estimator relies on offline and online phases. The offline phase utilizes past input-output data to estimate a set of possible coefficients of the polynomial system. Then, using this estimated set of coefficients and the side information about the system, the online phase provides a set estimate of the state. Finally, the proposed methodology is evaluated through its application on SIR (Susceptible, Infected, Recovered) epidemic model.
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| 2. |
- Farjadnia, Mahsa, et al.
(author)
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Robust data-driven predictive control of unknown nonlinear systems using reachability analysis
- 2023
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In: European Journal of Control. - : Elsevier BV. - 0947-3580.
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Journal article (peer-reviewed)abstract
- This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.
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| 3. |
- Jowett, Geraldine M., et al.
(author)
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ILC1 drive intestinal epithelial and matrix remodelling
- 2021
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In: Nature Materials. - : Springer Nature. - 1476-1122 .- 1476-4660. ; 20:2, s. 250-259
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Journal article (peer-reviewed)abstract
- Organoids can shed light on the dynamic interplay between complex tissues and rare cell types within a controlled microenvironment. Here, we develop gut organoid cocultures with type-1 innate lymphoid cells (ILC1) to dissect the impact of their accumulation in inflamed intestines. We demonstrate that murine and human ILC1 secrete transforming growth factor β1, driving expansion of CD44v6+ epithelial crypts. ILC1 additionally express MMP9 and drive gene signatures indicative of extracellular matrix remodelling. We therefore encapsulated human epithelial–mesenchymal intestinal organoids in MMP-sensitive, synthetic hydrogels designed to form efficient networks at low polymer concentrations. Harnessing this defined system, we demonstrate that ILC1 drive matrix softening and stiffening, which we suggest occurs through balanced matrix degradation and deposition. Our platform enabled us to elucidate previously undescribed interactions between ILC1 and their microenvironment, which suggest that they may exacerbate fibrosis and tumour growth when enriched in inflamed patient tissues.
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| 4. |
- Li, Zishuo, et al.
(author)
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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
- 2023
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In: IFAC-PapersOnLine. - : Elsevier BV. ; , s. 270-275
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Conference paper (peer-reviewed)abstract
- This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.
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| 5. |
- Niazi, Muhammad Umar B., et al.
(author)
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Clustering-based average state observer design for large-scale network systems
- 2023
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In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 151, s. 110914-
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Journal article (peer-reviewed)abstract
- This paper addresses the aggregated monitoring problem for large-scale network systems with a few dedicated sensors. Full state estimation of such systems is often infeasible due to unobservability and/or computational infeasibility; therefore, through clustering and aggregation, a tractable representation of a network system, called a projected network system, is obtained for designing a minimum-order average state observer. This observer estimates the average states of the clusters, which are identified under explicit consideration of estimation error. Moreover, given the clustering, the proposed observer design algorithm exploits the structure of the estimation error dynamics to achieve computational tractability. Simulations show that the computation of the proposed algorithm is significantly faster than the usual H2/H infinity observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal.
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| 6. |
- Niazi, Muhammad Umar B., et al.
(author)
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Feedback Design for Devising Optimal Epidemic Control Policies
- 2023
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In: <em>IFAC-PapersOnLine</em>. - : Elsevier BV. ; , s. 4031-4036
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Conference paper (peer-reviewed)abstract
- This paper proposes a feedback design that effectively copes with uncertainties for reliable epidemic monitoring and control. There are several optimization-based methods to estimate the parameters of an epidemic model by utilizing past reported data. However, due to the possibility of noise in the data, the estimated parameters may not be accurate, thereby exacerbating the model uncertainty. To address this issue, we provide an observer design that enables robust state estimation of epidemic processes, even in the presence of uncertain models and noisy measurements. Using the estimated model and state, we then devise optimal control policies by minimizing a predicted cost functional. To demonstrate the effectiveness of our approach, we implement it on a modified SIR epidemic model. The results show that our proposed method is efficient in mitigating the uncertainties that may arise.
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| 7. |
- Niazi, Muhammad Umar B., et al.
(author)
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Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
- 2023
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In: 2023 American Control Conference , ACC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3048-3055
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Conference paper (peer-reviewed)abstract
- Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties.
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| 8. |
- Niazi, Muhammad Umar B., et al.
(author)
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Observer Design for the State Estimation of Epidemic Processes
- 2022
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In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4325-4332
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Conference paper (peer-reviewed)abstract
- Although an appropriate choice of measured state variables may ensure observability, designing state observers for the state estimation of epidemic models remains a challenging task. Epidemic spread is a nonlinear process, often modeled as the law of mass action, which is of a quadratic form; thus, on a compact domain, its Lipschitz constant turns out to be local and relatively large, which renders the Lipschitz-based design criteria of existing observer architectures infeasible. In this paper, a novel observer architecture is proposed for the state estimation of a class of nonlinear systems that encompasses the deterministic epidemic models. The proposed observer offers extra leverage to reduce the influence of nonlinearity in the estimation error dynamics, which is not possible in other Luenberger-like observers. Algebraic Riccati inequalities are derived as sufficient conditions for the asymptotic convergence of the estimation error to zero under local Lipschitz and generalized Lipschitz assumptions. Equivalent linear matrix inequality formulations of the algebraic Riccati inequalities are also provided. The efficacy of the proposed observer design is illustrated by its application on the celebrated SIDARTHE-V epidemic model.
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| 9. |
- Niazi, Muhammad Umar B., et al.
(author)
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Parameterization-Free Observer Design for Nonlinear Systems: Application to the State Estimation of Networked SIR Epidemics
- 2023
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In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1724-1729
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Conference paper (peer-reviewed)abstract
- Traditional observer design methods rely on certain properties of the system's nonlinearity, such as Lipschitz continuity, one-sided Lipschitzness, a bounded Jacobian, or quadratic boundedness. These properties are described by parameterized inequalities. However, enforcing these inequalities globally can lead to very large parameters, resulting in overly conservative observer design criteria. These criteria become infeasible for highly nonlinear applications, such as networked epidemic processes. In this paper, we present an observer design approach for estimating the state of nonlinear systems, without requiring any parameterization of the system's nonlinearities. The proposed observer design depends only on systems' matrices and applies to systems with any nonlinearity. We establish different design criteria for ensuring both asymptotic and exponential convergence of the estimation error to zero. To demonstrate the efficacy of our approach, we employ it for estimating the state of a networked SIR epidemic model. We show that, even in the presence of measurement noise, the observer can accurately estimate the epidemic state of each node in the network. To the best of our knowledge, the proposed observer is the first that is capable of estimating the state of networked SIR models.
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| 10. |
- Niazi, Muhammad Umar B., et al.
(author)
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Resilient set-based state estimation for linear time-invariant systems using zonotopes
- 2023
-
In: European Journal of Control. - : Elsevier BV. - 0947-3580 .- 1435-5671. ; 74
-
Journal article (peer-reviewed)abstract
- This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm's worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system.
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