T. Vincent, Peter J. Weddle, Aleksei La Rue, R. Kee
The monitoring and control of battery systems can be enhanced by data collection and analysis that provide insight into the internal behavior of the battery. A well-known example is electrochemical impedance spectroscopy (EIS), which is equivalent to estimating the frequency response of the battery impedance at a particular operating condition. System identification provides a method for implementing EIS using hardware commonly found in advanced battery-management systems. In this chapter, a possible implementation of online system identification is discussed and illustrated using both simulation and experimental data.
{"title":"In situ identification of electrochemical impedance spectra for Li-ion batteries","authors":"T. Vincent, Peter J. Weddle, Aleksei La Rue, R. Kee","doi":"10.1049/pbce123e_ch5","DOIUrl":"https://doi.org/10.1049/pbce123e_ch5","url":null,"abstract":"The monitoring and control of battery systems can be enhanced by data collection and analysis that provide insight into the internal behavior of the battery. A well-known example is electrochemical impedance spectroscopy (EIS), which is equivalent to estimating the frequency response of the battery impedance at a particular operating condition. System identification provides a method for implementing EIS using hardware commonly found in advanced battery-management systems. In this chapter, a possible implementation of online system identification is discussed and illustrated using both simulation and experimental data.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134232860","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}
In this chapter, an innovative approach to fault detection for nonlinear dynamic systems is proposed, based on the recently introduced quasi-local set membership-identification method, overcoming some relevant issues proper of the “classical” techniques. The approach is based on the direct identification from experimental data of a suitable filter and related uncertainty bounds. These bounds are used to detect when a change (e.g., a fault) has occurred in the dynamics of the system of interest. The main advantage of the approach compared to the existing methods is that it avoids the utilization of complex modeling and filter design procedures, since the filter/observer is directly designed from data. Other advantages are that the approach does not require to choose any threshold (as typically done in many “classical” techniques), and it is not affected by under-modeling problems. An experimental study regarding fault detection for a drone actuator is finally presented to demonstrate the effectiveness of the proposed approach.
{"title":"Set membership fault detection for nonlinear dynamic systems","authors":"Milad Karimshoushtari, L. Spagnolo, C. Novara","doi":"10.1049/pbce123e_ch12","DOIUrl":"https://doi.org/10.1049/pbce123e_ch12","url":null,"abstract":"In this chapter, an innovative approach to fault detection for nonlinear dynamic systems is proposed, based on the recently introduced quasi-local set membership-identification method, overcoming some relevant issues proper of the “classical” techniques. The approach is based on the direct identification from experimental data of a suitable filter and related uncertainty bounds. These bounds are used to detect when a change (e.g., a fault) has occurred in the dynamics of the system of interest. The main advantage of the approach compared to the existing methods is that it avoids the utilization of complex modeling and filter design procedures, since the filter/observer is directly designed from data. Other advantages are that the approach does not require to choose any threshold (as typically done in many “classical” techniques), and it is not affected by under-modeling problems. An experimental study regarding fault detection for a drone actuator is finally presented to demonstrate the effectiveness of the proposed approach.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121878577","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}
In this chapter, the problem of estimating in a model-free manner the H∞ norm of a linear dynamic system is discussed at a tutorial level. Two recently developed methods for addressing this problem ...
{"title":"Algorithms for data-driven H∞-norm estimation","authors":"C. Rojas, Matias I. Müller","doi":"10.1049/pbce123e_ch8","DOIUrl":"https://doi.org/10.1049/pbce123e_ch8","url":null,"abstract":"In this chapter, the problem of estimating in a model-free manner the H∞ norm of a linear dynamic system is discussed at a tutorial level. Two recently developed methods for addressing this problem ...","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131747640","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}
{"title":"Experimental modeling of a web-winding machine: LPV approaches","authors":"","doi":"10.1049/pbce123e_ch4","DOIUrl":"https://doi.org/10.1049/pbce123e_ch4","url":null,"abstract":"","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114943046","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}
L. Blanken, J. Zundert, R. Rozario, Nard Strijbosch, T. Oomen
Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.
{"title":"Multivariable iterative learning control: analysis and designs for engineering applications","authors":"L. Blanken, J. Zundert, R. Rozario, Nard Strijbosch, T. Oomen","doi":"10.1049/pbce123e_ch7","DOIUrl":"https://doi.org/10.1049/pbce123e_ch7","url":null,"abstract":"Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109801","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}
The frequency-domain methods that exist for investigating the behaviour of linear systems have become fundamental tools for the control systems engineer. However, due to the increasedperformance demands on today's industrial systems, the effects of certain nonlinearities can no longer be neglected in modern control applications; for such systems, direct application of these frequency-domain tools is not possible. In the current literature, however, frequency-domain methods exist where the underlying linear dynamics of a nonlinear system can be captured in an identification experiment; in this manner, the nonlinear system is replaced by a linear model with a noise source where a best linear approximation of the nonlinear system is obtained with an associated frequency-dependent uncertainty. With the frequency-domain data and uncertainty obtained from an identification experiment, robust control algorithms can then be used to ensure performance for the underlying linear system. This chapter presents a data-driven robust control strategy which implements a convex optimization algorithm to ensure the performance and closed-loop stability of a linear system that is subject to nonlinear distortions (by considering a model-reference objective). The effectiveness of the proposed data-driven method is illustrated by designing a controller for an inertial positioning system that possesses nonlinear torsional dynamics.
{"title":"Robust data-driven control of systems with nonlinear distortions","authors":"Achille Nicoletti, Christoph Kammer, A. Karimi","doi":"10.1049/pbce123e_ch13","DOIUrl":"https://doi.org/10.1049/pbce123e_ch13","url":null,"abstract":"The frequency-domain methods that exist for investigating the behaviour of linear systems have become fundamental tools for the control systems engineer. However, due to the increasedperformance demands on today's industrial systems, the effects of certain nonlinearities can no longer be neglected in modern control applications; for such systems, direct application of these frequency-domain tools is not possible. In the current literature, however, frequency-domain methods exist where the underlying linear dynamics of a nonlinear system can be captured in an identification experiment; in this manner, the nonlinear system is replaced by a linear model with a noise source where a best linear approximation of the nonlinear system is obtained with an associated frequency-dependent uncertainty. With the frequency-domain data and uncertainty obtained from an identification experiment, robust control algorithms can then be used to ensure performance for the underlying linear system. This chapter presents a data-driven robust control strategy which implements a convex optimization algorithm to ensure the performance and closed-loop stability of a linear system that is subject to nonlinear distortions (by considering a model-reference objective). The effectiveness of the proposed data-driven method is illustrated by designing a controller for an inertial positioning system that possesses nonlinear torsional dynamics.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132065180","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}
Fei Xiong, Y. Cheng, O. Camps, M. Sznaier, C. Lagoa
This chapter addresses the problem of nonparametric identification of Wiener systems using a Kernel-based approach. Salient features of the proposed framework are its ability to exploit both positive and negative samples, and the fact that it does not require prior knowledge of the dimension of the output of the linear subsystem. Thus, it can be considered as a generalization to dynamical systems of kernel-based nonlinear manifold embedding methods recently developed in the machine-learning field. The main result of the chapter shows that while in principle, the proposed approach results in a non-convex problem, a tractable convex relaxation can be obtained by using a combination of polynomial optimization and rank-minimization techniques. The main advantage of the proposed algorithm stems from the fact that, since it is based on kernel ideas, it uses scalar inner products of the observed data, rather than the data itself. Hence, it can comfortably handle cases involving systems with high dimensional outputs. A practical scenario where such situation arises is activity classification from video data, since here each data point is a frame in a video sequence, and hence its dimension is typically O(103) even when using low resolution videos.
{"title":"A kernel-based approach to supervised nonparametric identification of Wiener systems","authors":"Fei Xiong, Y. Cheng, O. Camps, M. Sznaier, C. Lagoa","doi":"10.1049/pbce123e_ch2","DOIUrl":"https://doi.org/10.1049/pbce123e_ch2","url":null,"abstract":"This chapter addresses the problem of nonparametric identification of Wiener systems using a Kernel-based approach. Salient features of the proposed framework are its ability to exploit both positive and negative samples, and the fact that it does not require prior knowledge of the dimension of the output of the linear subsystem. Thus, it can be considered as a generalization to dynamical systems of kernel-based nonlinear manifold embedding methods recently developed in the machine-learning field. The main result of the chapter shows that while in principle, the proposed approach results in a non-convex problem, a tractable convex relaxation can be obtained by using a combination of polynomial optimization and rank-minimization techniques. The main advantage of the proposed algorithm stems from the fact that, since it is based on kernel ideas, it uses scalar inner products of the observed data, rather than the data itself. Hence, it can comfortably handle cases involving systems with high dimensional outputs. A practical scenario where such situation arises is activity classification from video data, since here each data point is a frame in a video sequence, and hence its dimension is typically O(103) even when using low resolution videos.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124370195","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}
In this chapter, we compare two approaches to the data-driven control (DDC) design problem. In this framework, the controllers are directly identified from data avoiding the plant identification step. The analyzed approaches are virtual reference feedback tuning (VRFT) and set-membership tuning (SMT) controller. They differ in the assumptions about the noise affecting the experimental data and the criteria to select an optimal controller. The former strategy assumes an stochastic description of the unknown signals, while the latter imposes an unknown but bounded (UBB) noise structure. Both methodologies are described and their main theoretical results are reported. The two approaches are evaluated on an experimental case study, consisting of the controller tuning for an active suspension (AS) system. Three Monte Carlo experiments are performed, where 100 controllers are derived from data affected by measurement noise using both methods, and their performance is evaluated on the experimental test-bench. Results show that both approaches offer a similar performance when the size of the dataset is much larger than the dimension of the controller parameters vector. However, for reduced datasets, the SMT approach gives consistent results while the VRFT method is not able to extract useful information. The same behavior is observed when the two approaches are applied to datasets affected by process disturbances. It is observed that the root mean squared error of the resulting loops can be up to 30 times lower using the set membership method for reduced datasets.
{"title":"A comparative study of VRFT and set-membership data-driven controller design techniques: active suspension tuning case","authors":"F. Valderrama, F. Ruiz","doi":"10.1049/pbce123e_ch9","DOIUrl":"https://doi.org/10.1049/pbce123e_ch9","url":null,"abstract":"In this chapter, we compare two approaches to the data-driven control (DDC) design problem. In this framework, the controllers are directly identified from data avoiding the plant identification step. The analyzed approaches are virtual reference feedback tuning (VRFT) and set-membership tuning (SMT) controller. They differ in the assumptions about the noise affecting the experimental data and the criteria to select an optimal controller. The former strategy assumes an stochastic description of the unknown signals, while the latter imposes an unknown but bounded (UBB) noise structure. Both methodologies are described and their main theoretical results are reported. The two approaches are evaluated on an experimental case study, consisting of the controller tuning for an active suspension (AS) system. Three Monte Carlo experiments are performed, where 100 controllers are derived from data affected by measurement noise using both methods, and their performance is evaluated on the experimental test-bench. Results show that both approaches offer a similar performance when the size of the dataset is much larger than the dimension of the controller parameters vector. However, for reduced datasets, the SMT approach gives consistent results while the VRFT method is not able to extract useful information. The same behavior is observed when the two approaches are applied to datasets affected by process disturbances. It is observed that the root mean squared error of the resulting loops can be up to 30 times lower using the set membership method for reduced datasets.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"40 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124851531","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}
The Fisher information matrix (FIM) has long been of interest in statistics and other areas. It is widely used to measure the amount of information and calculate the lower bound for the variance for maximum likelihood estimation (MLE). In practice, we do not always know the actual FIM. This is often because obtaining the firstor second-order derivative of the log-likelihood function is difficult, or simply because the calculation of FIM is too formidable. In such cases, we need to utilize the approximation of FIM. In general, there are two ways to estimate FIM. One is to use the product of gradient and the transpose of itself, and the other is to calculate the Hessian matrix and then take negative sign. Mostly people use the latter method in practice. However, this is not necessarily the optimal way. To find out which of the two methods is better, we need to conduct a theoretical study to compare their efficiency. In this paper, we mainly focus on the case where the unknown parameter that needs to be estimated by MLE is scalar, and the random variables we have are independent. In this scenario, FIM is virtually Fisher information number (FIN). Using the Central Limit Theorem (CLT), we get asymptotic variances for the two methods, by which we compare their accuracy. Taylor expansion assists in estimating the two asymptotic variances. A numerical study is provided as an illustration of the conclusion. The next is a summary of limitations of this paper. We also enumerate several fields of interest for future study in the end of this paper.
{"title":"Relative accuracy of two methods for approximating observed Fisher information","authors":"Shenghan Guo, J. Spall","doi":"10.1049/pbce123e_ch10","DOIUrl":"https://doi.org/10.1049/pbce123e_ch10","url":null,"abstract":"The Fisher information matrix (FIM) has long been of interest in statistics and other areas. It is widely used to measure the amount of information and calculate the lower bound for the variance for maximum likelihood estimation (MLE). In practice, we do not always know the actual FIM. This is often because obtaining the firstor second-order derivative of the log-likelihood function is difficult, or simply because the calculation of FIM is too formidable. In such cases, we need to utilize the approximation of FIM. In general, there are two ways to estimate FIM. One is to use the product of gradient and the transpose of itself, and the other is to calculate the Hessian matrix and then take negative sign. Mostly people use the latter method in practice. However, this is not necessarily the optimal way. To find out which of the two methods is better, we need to conduct a theoretical study to compare their efficiency. In this paper, we mainly focus on the case where the unknown parameter that needs to be estimated by MLE is scalar, and the random variables we have are independent. In this scenario, FIM is virtually Fisher information number (FIN). Using the Central Limit Theorem (CLT), we get asymptotic variances for the two methods, by which we compare their accuracy. Taylor expansion assists in estimating the two asymptotic variances. A numerical study is provided as an illustration of the conclusion. The next is a summary of limitations of this paper. We also enumerate several fields of interest for future study in the end of this paper.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133218558","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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