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To keep up with the ever increasing demand on performance and efficiency of control systems, accurate models are needed. System identification is concerned with the estimation and validation of mathematical models of dynamical systems from experimental data. The main problem considered in this thesis is how to take advantage of structural information in system identification. Including this additional information can significantly improve the quality of the identified model.First, the problem of input design for networked systems is considered. Results from closed-loop input design are generalized to the networked case. The main difference between the networked setting and the classical open- or closed-loop setting is the possibility of using measurable, or known, disturbances to improve the excitation. Such disturbances cannot be affected during the experiment and are common in industrial applications.A framework to include the additional information about the measurable disturbances in the input design is presented. The framework is evaluated in two simulation examples and several interesting observations are made.Second, the result from an input design is often the correlation properties of the input signal.The question is then how to generate the input signal that can be applied to the system with the given properties. This thesis presents a novel signal generation method that is able to handle input and output constraints. In industrial applications, it is often vital to satisfy constraints on both the input and the output signals during the system identification experiment. The method is formulated as a reference tracking Model Predictive Controller (MPC), with the desired correlation properties of the signal as reference, while satisfying the input and output constraints on the considered system. The core of the algorithm is the formulation of the signal generation as an MPC which allows the use existing tools to make the algorithm robust and adaptive. The, QC 20140929

To keep up with the ever increasing demand on performance and efficiency of control systems, accurate models are needed. System identification is concerned with the estimation and validation of mathematical models of dynamical systems from experimental data. The main problem considered in this thesis is how to take advantage of structural information in system identification. Including this additional information can significantly improve the quality of the identified model.First, the problem of input design for networked systems is considered. Results from closed-loop input design are generalized to the networked case. The main difference between the networked setting and the classical open- or closed-loop setting is the possibility of using measurable, or known, disturbances to improve the excitation. Such disturbances cannot be affected during the experiment and are common in industrial applications.A framework to include the additional information about the measurable disturbances in the input design is presented. The framework is evaluated in two simulation examples and several interesting observations are made.Second, the result from an input design is often the correlation properties of the input signal.The question is then how to generate the input signal that can be applied to the system with the given properties. This thesis presents a novel signal generation method that is able to handle input and output constraints. In industrial applications, it is often vital to satisfy constraints on both the input and the output signals during the system identification experiment. The method is formulated as a reference tracking Model Predictive Controller (MPC), with the desired correlation properties of the signal as reference, while satisfying the input and output constraints on the considered system. The core of the algorithm is the formulation of the signal generation as an MPC which allows the use existing tools to make the algorithm robust and adaptive. The, QC 20140929

To keep up with the ever increasing demand on performance and efficiency of control systems, accurate models are needed. System identification is concerned with the estimation and validation of mathematical models of dynamical systems from experimental data. The main problem considered in this thesis is how to take advantage of structural information in system identification. Including this additional information can significantly improve the quality of the identified model.First, the problem of input design for networked systems is considered. Results from closed-loop input design are generalized to the networked case. The main difference between the networked setting and the classical open- or closed-loop setting is the possibility of using measurable, or known, disturbances to improve the excitation. Such disturbances cannot be affected during the experiment and are common in industrial applications.A framework to include the additional information about the measurable disturbances in the input design is presented. The framework is evaluated in two simulation examples and several interesting observations are made.Second, the result from an input design is often the correlation properties of the input signal.The question is then how to generate the input signal that can be applied to the system with the given properties. This thesis presents a novel signal generation method that is able to handle input and output constraints. In industrial applications, it is often vital to satisfy constraints on both the input and the output signals during the system identification experiment. The method is formulated as a reference tracking Model Predictive Controller (MPC), with the desired correlation properties of the signal as reference, while satisfying the input and output constraints on the considered system. The core of the algorithm is the formulation of the signal generation as an MPC which allows the use existing tools to make the algorithm robust and adaptive. The, QC 20140929

To keep up with the ever increasing demand on performance and efficiency of control systems, accurate models are needed. System identification is concerned with the estimation and validation of mathematical models of dynamical systems from experimental data. The main problem considered in this thesis is how to take advantage of structural information in system identification. Including this additional information can significantly improve the quality of the identified model.First, the problem of input design for networked systems is considered. Results from closed-loop input design are generalized to the networked case. The main difference between the networked setting and the classical open- or closed-loop setting is the possibility of using measurable, or known, disturbances to improve the excitation. Such disturbances cannot be affected during the experiment and are common in industrial applications.A framework to include the additional information about the measurable disturbances in the input design is presented. The framework is evaluated in two simulation examples and several interesting observations are made.Second, the result from an input design is often the correlation properties of the input signal.The question is then how to generate the input signal that can be applied to the system with the given properties. This thesis presents a novel signal generation method that is able to handle input and output constraints. In industrial applications, it is often vital to satisfy constraints on both the input and the output signals during the system identification experiment. The method is formulated as a reference tracking Model Predictive Controller (MPC), with the desired correlation properties of the signal as reference, while satisfying the input and output constraints on the considered system. The core of the algorithm is the formulation of the signal generation as an MPC which allows the use existing tools to make the algorithm robust and adaptive. The, QC 20140929

To keep up with the ever increasing demand on performance and efficiency of control systems, accurate models are needed. System identification is concerned with the estimation and validation of mathematical models of dynamical systems from experimental data. The main problem considered in this thesis is how to take advantage of structural information in system identification. Including this additional information can significantly improve the quality of the identified model.First, the problem of input design for networked systems is considered. Results from closed-loop input design are generalized to the networked case. The main difference between the networked setting and the classical open- or closed-loop setting is the possibility of using measurable, or known, disturbances to improve the excitation. Such disturbances cannot be affected during the experiment and are common in industrial applications.A framework to include the additional information about the measurable disturbances in the input design is presented. The framework is evaluated in two simulation examples and several interesting observations are made.Second, the result from an input design is often the correlation properties of the input signal.The question is then how to generate the input signal that can be applied to the system with the given properties. This thesis presents a novel signal generation method that is able to handle input and output constraints. In industrial applications, it is often vital to satisfy constraints on both the input and the output signals during the system identification experiment. The method is formulated as a reference tracking Model Predictive Controller (MPC), with the desired correlation properties of the signal as reference, while satisfying the input and output constraints on the considered system. The core of the algorithm is the formulation of the signal generation as an MPC which allows the use existing tools to make the algorithm robust and adaptive. The, QC 20140929

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed. Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification. Two special structures are considered, the cascade and the parallel serial structure. The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems. The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods. The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice, QC 20120416

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed. Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification. Two special structures are considered, the cascade and the parallel serial structure. The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems. The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods. The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice, QC 20120416

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed. Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification. Two special structures are considered, the cascade and the parallel serial structure. The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems. The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods. The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice, QC 20120416

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed. Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification. Two special structures are considered, the cascade and the parallel serial structure. The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems. The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods. The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice, QC 20120416

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed. Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification. Two special structures are considered, the cascade and the parallel serial structure. The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems. The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods. The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice, QC 20120416