In applications such as wind energy, industrial robotics, and chemical processing, increases in complexity and automation have made component malfunctions and other abnormal events (i.e., faults) an ever-present threa...
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In applications such as wind energy, industrial robotics, and chemical processing, increases in complexity and automation have made component malfunctions and other abnormal events (i.e., faults) an ever-present threat to safety and reliability. Thus, fault detection algorithms have become an essential feature of modern control systems, leading to significant decreases in downtime, maintenance costs, and catastrophic failures. However, while well-established statistical methods are effective in many cases, they often fail to make the critical distinction between faults and normal process disturbances. An attractive alternative is to exploit detailed process models that, at least in principle, can be used to characterize the outputs consistent with normal operation, providing a rigorous basis for fault detection. Methods that furnish a guaranteed enclosure of these outputs (e.g., using set-basedstate estimators) are particularly attractive because they eliminate the possibility of costly false alarms and provide better trade-offs between false alarms and missed faults. However, such methods are currently impractical for systems with strong nonlinearities or large uncertainties. For such systems, existing set-basedestimation techniques often produce enclosures that are far too conservative to be useful for fault detection, or avoid this only at excessive computational cost. Thus, there is a critical need for advanced algorithms that can rapidly detect faults for realistic nonlinear systems, and do so rigorously in the presence of disturbances, measurement noise, and large model uncertainties. In this thesis, we develop an advanced set-based state estimation method for uncertain nonlinear systems, and demonstrate its application to provide fast and accurate fault detection for such systems. Our proposed estimation method is performed recursively in two steps. First, the prediction step computes an enclosure of the possible model outputs under uncertainty over one di
set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the r...
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ISBN:
(纸本)9781713872344
set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the representation of external disturbances and imperfectly known parameters by finitely large sets. In contrast to stochastic counterparts, often employing one of the available variants of Kalman filters, set-based approaches are less widely used. The reason for this observation is the fact that naive implementations often suffer from a non-negligible degree of overestimation and that (unless certain monotonicity properties are satisfied) set-based computations come with a notable increase of the computational complexity, resulting among others from required interval splitting procedures. This paper tries to resolve both issues by means of an ellipsoidal implementation of a discrete-time set-valued stateestimation procedure that is validated experimentally and compared with an Unscented Kalman Filter (UKF) for a laboratory-scale magnetic levitation system. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
This paper proposes a robust nonlinear model predictive control based on nominal predictions with tighter constraints derived from a zonotope-based disturbance propagation. The worst-case disturbance reachable sets ar...
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This paper proposes a robust nonlinear model predictive control based on nominal predictions with tighter constraints derived from a zonotope-based disturbance propagation. The worst-case disturbance reachable sets are computed from zonotopes combined with the mean-value theorem applied to the nonlinear model, which reduces the conservativeness of the tighter constraints. Mean-value disturbance estimates are also incorporated into the prediction model in order to mitigate the effect of asymptotic constant disturbances while maintaining the recursive feasibility and stability of the control policy. The proposed disturbance propagation technique is applied to simulations of a DC-DC converter and a continually stirred tank reactor benchmark case studies to illustrate the benefits of the proposed approach.
set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the r...
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set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the representation of external disturbances and imperfectly known parameters by finitely large sets. In contrast to stochastic counterparts, often employing one of the available variants of Kalman filters, set-based approaches are less widely used. The reason for this observation is the fact that naive implementations often suffer from a non-negligible degree of overestimation and that (unless certain monotonicity properties are satisfied) set-based computations come with a notable increase of the computational complexity, resulting among others from required interval splitting procedures. This paper tries to resolve both issues by means of an ellipsoidal implementation of a discrete-time set-valued stateestimation procedure that is validated experimentally and compared with an Unscented Kalman Filter (UKF) for a laboratory-scale magnetic levitation system.
Robust stateestimation is addressed in a noisy environment and within a distributed and networked architecture. Both bounded disturbances and random noises are considered. A Distributed Zonotopic and Gaussian Kalman ...
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Robust stateestimation is addressed in a noisy environment and within a distributed and networked architecture. Both bounded disturbances and random noises are considered. A Distributed Zonotopic and Gaussian Kalman Filter (DZG-KF) is proposed where each network node implements a local state estimator using symbolic Zonotopes and Gaussian noise Mergers (s-ZGM), a class of set-membership and Probabilistic Mergers (SPM). Each network node communicates its own state information only to its neighbours. The proposed system includes a dedicated service called Unique Symbols Provider (USP) giving unique identifiers. It also includes Matrices with Labelled Columns (MLC) featuring column-wise sparsity, and symbolic zonotopes (s-zonotopes). This significantly enhances the propagation of uncertainties and preserves global dependencies that would otherwise be lost (or impeded) by the peer-to-peer communication through the network. A number of other network-related constraints can be managed within this framework. Numerical simulations show significant improvements compared to a non-symbolic approach.
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