Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867568
S. Belikov
Extraction of quantitative nanomechanical data in AFM Resonance modes, such as Amplitude and Frequency Modulation, is a challenging task. It requires either restoration of the force curve for analysis or using experimental data from the Resonant modes directly to estimate parameters of the unknown force. In many situations, force restoration is preferable, because direct parameter estimation methods work only if adequate parametric force model is available (which is rarely the case). At the same time, the force curve provides the most valuable source for material characterization, even when not parameterized. This paper describes a novel approach to force curve restoration from AFM Resonant mode experimental data. The approach is based on Krylov-Bogoliubov-Mitropolsky (KBM) asymptotic dynamics of AFM. Tikhonov regularization is used in case of noisy measurements, when the problem of force restoration becomes ill-posed.
{"title":"Force Curves Restoration in Atomic Force Microscopy (AFM) Resonant Modes*","authors":"S. Belikov","doi":"10.23919/ACC53348.2022.9867568","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867568","url":null,"abstract":"Extraction of quantitative nanomechanical data in AFM Resonance modes, such as Amplitude and Frequency Modulation, is a challenging task. It requires either restoration of the force curve for analysis or using experimental data from the Resonant modes directly to estimate parameters of the unknown force. In many situations, force restoration is preferable, because direct parameter estimation methods work only if adequate parametric force model is available (which is rarely the case). At the same time, the force curve provides the most valuable source for material characterization, even when not parameterized. This paper describes a novel approach to force curve restoration from AFM Resonant mode experimental data. The approach is based on Krylov-Bogoliubov-Mitropolsky (KBM) asymptotic dynamics of AFM. Tikhonov regularization is used in case of noisy measurements, when the problem of force restoration becomes ill-posed.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130681272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867695
Qiushi Huang, Chenye Wu
State estimation is crucial to the reliable operation of the power grid. Hence, various cyber-physical attacks take advantage of manipulating the state estimation outcome to threaten grid reliability. Such cyber-physical attacks include fuzzing, malware injection and false data injection attack (FDIA). While the traditional residual-based error detection could prevent certain attacks, FDIA is not one of them. This study notices that matrix separation is a powerful tool in terms of FDIA detection. Thus, we cast FDIA detection into the matrix separation framework, embedding two types of structural knowledge. The first one highlights that only some rows in the attack matrix have nonzero values, while the second one emphasizes that the temporal variability of data collected by the same meter is usually small. Our proposed framework yields a structure embedding detection method, and numerical studies highlight its remarkable performance.
{"title":"Boosting False Data Injection Attack Detection with Structural Knowledge","authors":"Qiushi Huang, Chenye Wu","doi":"10.23919/ACC53348.2022.9867695","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867695","url":null,"abstract":"State estimation is crucial to the reliable operation of the power grid. Hence, various cyber-physical attacks take advantage of manipulating the state estimation outcome to threaten grid reliability. Such cyber-physical attacks include fuzzing, malware injection and false data injection attack (FDIA). While the traditional residual-based error detection could prevent certain attacks, FDIA is not one of them. This study notices that matrix separation is a powerful tool in terms of FDIA detection. Thus, we cast FDIA detection into the matrix separation framework, embedding two types of structural knowledge. The first one highlights that only some rows in the attack matrix have nonzero values, while the second one emphasizes that the temporal variability of data collected by the same meter is usually small. Our proposed framework yields a structure embedding detection method, and numerical studies highlight its remarkable performance.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123488375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867569
Timothy L. Molloy, G. Nair
Active state estimation is the problem of controlling a partially observed Markov decision process (POMDP) to minimize the uncertainty associated with its latent states. Selecting meaningful, yet tractable, measures of uncertainty to optimize is a key challenge in active state estimation, with the vast majority of popular uncertainty measures leading to POMDP costs that are nonlinear in the belief state, which makes them difficult (and often impossible) to optimize directly using standard POMDP solvers. To address this challenge, in this paper we propose the joint entropy of the state, observation, and control trajectories of POMDPs as a novel tractable uncertainty measure for active state estimation. By expressing the joint entropy in stage-additive form, we show that joint-entropy-minimization (JEM) problems can be reformulated as standard POMDPs with cost functions that are linear in the belief state. Linearity of the costs is of considerable practical significance since it enables the solution of our JEM problems directly using standard POMDP solvers. We illustrate JEM in simulations where it reduces the probability of error in state trajectory estimates whilst being more computationally efficient than competing active state estimation formulations.
{"title":"JEM: Joint Entropy Minimization for Active State Estimation with Linear POMDP Costs","authors":"Timothy L. Molloy, G. Nair","doi":"10.23919/ACC53348.2022.9867569","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867569","url":null,"abstract":"Active state estimation is the problem of controlling a partially observed Markov decision process (POMDP) to minimize the uncertainty associated with its latent states. Selecting meaningful, yet tractable, measures of uncertainty to optimize is a key challenge in active state estimation, with the vast majority of popular uncertainty measures leading to POMDP costs that are nonlinear in the belief state, which makes them difficult (and often impossible) to optimize directly using standard POMDP solvers. To address this challenge, in this paper we propose the joint entropy of the state, observation, and control trajectories of POMDPs as a novel tractable uncertainty measure for active state estimation. By expressing the joint entropy in stage-additive form, we show that joint-entropy-minimization (JEM) problems can be reformulated as standard POMDPs with cost functions that are linear in the belief state. Linearity of the costs is of considerable practical significance since it enables the solution of our JEM problems directly using standard POMDP solvers. We illustrate JEM in simulations where it reduces the probability of error in state trajectory estimates whilst being more computationally efficient than competing active state estimation formulations.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114320022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867737
M. McCourt, Zachary I. Bell, Scott A. Nivison
A passivity-based switched systems analysis of an estimator-predictor framework is presented for mobile target tracking. Measurements of the target are provided by a mobile sensor with limited field-of-view (e.g., a camera). The analysis demonstrates that passivity-based dwell-times exist to ensure the tracking objective is achieved, despite intermittent feedback of the target pose. Specifically, a maximum ratio is determined between the length of time the target is unobserved and the length of time the target is observed. After a period of time where the target is unobserved, this provides a minimum time that the target must be observed to ensure passivity of the system. An additional result demonstrates conditions under which the estimator-predictor framework is uniformly ultimately bounded. These results show that the framework is robust to intermittent feedback, which is an alternative approach to relaxing the continuous observation constraint required by many existing results.
{"title":"Passivity-Based Target Tracking Robust to Intermittent Measurements","authors":"M. McCourt, Zachary I. Bell, Scott A. Nivison","doi":"10.23919/ACC53348.2022.9867737","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867737","url":null,"abstract":"A passivity-based switched systems analysis of an estimator-predictor framework is presented for mobile target tracking. Measurements of the target are provided by a mobile sensor with limited field-of-view (e.g., a camera). The analysis demonstrates that passivity-based dwell-times exist to ensure the tracking objective is achieved, despite intermittent feedback of the target pose. Specifically, a maximum ratio is determined between the length of time the target is unobserved and the length of time the target is observed. After a period of time where the target is unobserved, this provides a minimum time that the target must be observed to ensure passivity of the system. An additional result demonstrates conditions under which the estimator-predictor framework is uniformly ultimately bounded. These results show that the framework is robust to intermittent feedback, which is an alternative approach to relaxing the continuous observation constraint required by many existing results.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116184163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867525
Jianqi Chen, T. Qi, Yanling Ding, Hui Peng, Jing Chen, S. Hara
In this paper we study the stability and stabilizability problems of multi-input, multi-output linear time-invariant systems subject to stochastic multiplicative uncertainties under the mean-square criterion. We consider the matrix-valued unstructured perturbations, which consist of static, zero-mean stochastic processes. We first obtain a necessary and sufficient condition to ensure the stability of the open-loop stable system against uncertainties in the mean-square sense. Based on the obtained mean-square stability condition, we further answer the question: How can an open-loop unstable system be stabilized by output feedback in the mean-square sense despite the presence of such stochastic uncertainties? The complete and explicit stabilizability conditions are derived, which reveal how the locations and directions associated with unstable poles and nonminimum phase zeros of the plant coupled together affect the mean-square stabilizability.
{"title":"Mean-Square Stabilizability Under Unstructured Stochastic Multiplicative Uncertainties: A Mean-Square Small-Gain Perspective","authors":"Jianqi Chen, T. Qi, Yanling Ding, Hui Peng, Jing Chen, S. Hara","doi":"10.23919/ACC53348.2022.9867525","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867525","url":null,"abstract":"In this paper we study the stability and stabilizability problems of multi-input, multi-output linear time-invariant systems subject to stochastic multiplicative uncertainties under the mean-square criterion. We consider the matrix-valued unstructured perturbations, which consist of static, zero-mean stochastic processes. We first obtain a necessary and sufficient condition to ensure the stability of the open-loop stable system against uncertainties in the mean-square sense. Based on the obtained mean-square stability condition, we further answer the question: How can an open-loop unstable system be stabilized by output feedback in the mean-square sense despite the presence of such stochastic uncertainties? The complete and explicit stabilizability conditions are derived, which reveal how the locations and directions associated with unstable poles and nonminimum phase zeros of the plant coupled together affect the mean-square stabilizability.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121499246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867604
Xiaoke Wang, W. Ohnishi, T. Atsumi
For hard disk drive (HDD) systems, designing a robust controller that achieves favorable disturbance rejection is crucial in increasing the precision of positioning of the magnetic head and the storage of HDD. This research presents a frequency response data-based convex optimization method to design a form-fixed shaping filter which guarantees robust performance and minimizes 2 norm of the error signal for a perturbed SISO system.
{"title":"Robust controller design based on convex optimization and RCBode plots using frequency response data: Application to hard disk drive systems","authors":"Xiaoke Wang, W. Ohnishi, T. Atsumi","doi":"10.23919/ACC53348.2022.9867604","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867604","url":null,"abstract":"For hard disk drive (HDD) systems, designing a robust controller that achieves favorable disturbance rejection is crucial in increasing the precision of positioning of the magnetic head and the storage of HDD. This research presents a frequency response data-based convex optimization method to design a form-fixed shaping filter which guarantees robust performance and minimizes 2 norm of the error signal for a perturbed SISO system.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121568469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867272
Dorijan Leko, M. Vašak
The paper provides a data integrity cyber-attack detection framework based on minimum robust positively invariant sets. A general linear control system with a Kalman filter is considered. The set localization of the state estimator error is taken into account for developing the attack detector. An intelligent attacker algorithm is developed that has access to a subset of signals from the sensor and actuator channel of the control system. It is assumed that the attacker possesses the entire control system model to perform the most proficient attack for a certain set-up of data availability and compromisation. The attacker compromises a set of measurement data under the constraint of remaining non-discovered by the detector. The presented methodology allows assessing the effectiveness of the control system defense achievable in various data integrity attack scenarios. The developed detector and attacker algorithm were implemented on an illustrative example of a power system with two control areas and automatic generation control.
{"title":"Minimum Robust Invariant Sets and Kalman Filtering in Cyber Attacking and Defending","authors":"Dorijan Leko, M. Vašak","doi":"10.23919/ACC53348.2022.9867272","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867272","url":null,"abstract":"The paper provides a data integrity cyber-attack detection framework based on minimum robust positively invariant sets. A general linear control system with a Kalman filter is considered. The set localization of the state estimator error is taken into account for developing the attack detector. An intelligent attacker algorithm is developed that has access to a subset of signals from the sensor and actuator channel of the control system. It is assumed that the attacker possesses the entire control system model to perform the most proficient attack for a certain set-up of data availability and compromisation. The attacker compromises a set of measurement data under the constraint of remaining non-discovered by the detector. The presented methodology allows assessing the effectiveness of the control system defense achievable in various data integrity attack scenarios. The developed detector and attacker algorithm were implemented on an illustrative example of a power system with two control areas and automatic generation control.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121751919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867870
Ashfaq Iftakher, Chinmay M. Aras, Mohammed Sadaf Monjur, M. Hasan
We present a data-driven surrogate modeling technique to replace computationally expensive high-fidelity models. The proposed technique uses the Hessian information of the original grey-box/black-box model to construct edge-concave underestimators and edge-convex overestimators to provide approximation over the entire domain with guaranteed error-bounds. A surrogate model with prepostulated form is then achieved by performing a parameter estimation that ensures the approximation to be bounded between the vertex polyhedral under- and over-estimators of the original model. We describe a package named GEMS that integrates and automates the required series of tasks, i.e., the location and the number of sample evaluation, estimation of the Hessian bounds, and parameter estimation to obtain the surrogate with guaranteed prediction within the error bounds. As a case study, we demonstrate that the suggested surrogate by GEMS exhibits good performance in predicting the solubility of hydrofluorocarbon (HFC) refrigerants in ionic liquids (IL).
{"title":"A Framework for Guaranteed Error-bounded Surrogate Modeling","authors":"Ashfaq Iftakher, Chinmay M. Aras, Mohammed Sadaf Monjur, M. Hasan","doi":"10.23919/ACC53348.2022.9867870","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867870","url":null,"abstract":"We present a data-driven surrogate modeling technique to replace computationally expensive high-fidelity models. The proposed technique uses the Hessian information of the original grey-box/black-box model to construct edge-concave underestimators and edge-convex overestimators to provide approximation over the entire domain with guaranteed error-bounds. A surrogate model with prepostulated form is then achieved by performing a parameter estimation that ensures the approximation to be bounded between the vertex polyhedral under- and over-estimators of the original model. We describe a package named GEMS that integrates and automates the required series of tasks, i.e., the location and the number of sample evaluation, estimation of the Hessian bounds, and parameter estimation to obtain the surrogate with guaranteed prediction within the error bounds. As a case study, we demonstrate that the suggested surrogate by GEMS exhibits good performance in predicting the solubility of hydrofluorocarbon (HFC) refrigerants in ionic liquids (IL).","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124463735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867535
Mohamad T. Shahab, Kévin Garanger, E. Feron
In this paper, we consider the problem of controlling a rigid assembly of aerial vehicles under uncertainty. We consider the case when the positions of the vehicle modules in the assembly structure are unknown, but belong to a finite set. In addition, we consider that each module has only its own measurements available for feedback but not that of the whole assembly, so a decentralized control law is developed. We apply an adaptive switching control approach to control this uncertain system. Given a stabilizing controller for the case when there is no uncertainty, we show that the proposed adaptive approach achieves the control objective under uncertainty by presenting illustrative simulation examples; we provide a case study of a recently proposed novel modular flying system, namely a fractal tetrahedron assembly.
{"title":"Control of an Assembly of Aerial Vehicles Under Uncertainty","authors":"Mohamad T. Shahab, Kévin Garanger, E. Feron","doi":"10.23919/ACC53348.2022.9867535","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867535","url":null,"abstract":"In this paper, we consider the problem of controlling a rigid assembly of aerial vehicles under uncertainty. We consider the case when the positions of the vehicle modules in the assembly structure are unknown, but belong to a finite set. In addition, we consider that each module has only its own measurements available for feedback but not that of the whole assembly, so a decentralized control law is developed. We apply an adaptive switching control approach to control this uncertain system. Given a stabilizing controller for the case when there is no uncertainty, we show that the proposed adaptive approach achieves the control objective under uncertainty by presenting illustrative simulation examples; we provide a case study of a recently proposed novel modular flying system, namely a fractal tetrahedron assembly.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124020933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867867
Ruochen Niu, Syed M. Hassaan, Sze Zheng Yong
In this paper, we consider the optimal input design problem for active model discrimination (AMD) among a set of switched nonlinear models that are constrained by metric/signal temporal logic specifications and affected by uncontrolled inputs and noise. To deal with nonlinear and non-convex constraints in the resulting bilevel optimization problem, we first over-approximate the nonlinear dynamics using piecewise affine abstractions. Then, we solve the relaxed inner problem of the bilevel AMD problem as parametric optimization problems and substitute the parametric solutions into the outer problem to obtain sufficient separating inputs for AMD. Moreover, since the parametric optimization problems are often computationally demanding, we propose several strategies to reduce the computational time, while preserving feasibility of the separating inputs for AMD. Finally, we demonstrate the effectiveness of our approach on several illustrative examples on fault detection and lane changing scenario.
{"title":"A Multi-Parametric Method for Active Model Discrimination of Nonlinear Systems with Temporal Logic-Constrained Switching","authors":"Ruochen Niu, Syed M. Hassaan, Sze Zheng Yong","doi":"10.23919/ACC53348.2022.9867867","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867867","url":null,"abstract":"In this paper, we consider the optimal input design problem for active model discrimination (AMD) among a set of switched nonlinear models that are constrained by metric/signal temporal logic specifications and affected by uncontrolled inputs and noise. To deal with nonlinear and non-convex constraints in the resulting bilevel optimization problem, we first over-approximate the nonlinear dynamics using piecewise affine abstractions. Then, we solve the relaxed inner problem of the bilevel AMD problem as parametric optimization problems and substitute the parametric solutions into the outer problem to obtain sufficient separating inputs for AMD. Moreover, since the parametric optimization problems are often computationally demanding, we propose several strategies to reduce the computational time, while preserving feasibility of the separating inputs for AMD. Finally, we demonstrate the effectiveness of our approach on several illustrative examples on fault detection and lane changing scenario.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126212311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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