“…More detailed contents, about the design of input signals and the position selection of vibration output signals, can be found in other references of on-orbit identification experiments. 4,5,9,12 Figure 3.Designed control torque input signal.Figure 4.Placements of sensors in satellite.…”
Section: Numerical Examplesmentioning
confidence: 99%
“…7,8 For instance, the solar panel rotation of the ETS-VIII can cause a maximum of 25% change in the structural parameter. 9 In addition, from the perspective of actual operations, we also expect that on-orbit identification experiments can be implemented without affecting the normal operating conditions of the spacecraft, thereby avoiding additional propellant consumption.…”
This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.
“…More detailed contents, about the design of input signals and the position selection of vibration output signals, can be found in other references of on-orbit identification experiments. 4,5,9,12 Figure 3.Designed control torque input signal.Figure 4.Placements of sensors in satellite.…”
Section: Numerical Examplesmentioning
confidence: 99%
“…7,8 For instance, the solar panel rotation of the ETS-VIII can cause a maximum of 25% change in the structural parameter. 9 In addition, from the perspective of actual operations, we also expect that on-orbit identification experiments can be implemented without affecting the normal operating conditions of the spacecraft, thereby avoiding additional propellant consumption.…”
This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.
A large amount of data are required for the recursive predictor-based subspace identification (RPBSID) algorithm when system state vector is estimated. In this paper, an improved RPBSID method is presented and applied to identify the spacecraft time-varying moment of inertia parameters. Comparing with the original RPBSID algorithm, the improved method does not need to construct the corresponding Hankel matrix for each time instant when solving the state vector. The recursive least squares is used to implement the recursive estimation of the state vector, thereby reducing the computation cost of the identification process. Then, the moment of inertia matrix parameters can be determined recursively from the system state-space model by matrix transformation. In numerical simulations, for a spacecraft with flexible appendages, three cases that may cause the moments of inertia to change with time are investigated. The simulation results illustrate that the proposed recursive method can identify the time-varying moment of inertia parameters effectively and has higher computational efficiency than that of classical RPBSID algorithm.
“…Better performance can be obtained by using a more flexible two-parameter controller [16]. Moreover, with the development of science and technology, two-parameter controller is also frequently used in practice in terms of aerospace [26], robotics [3], power systems [6], etc. Meanwhile, the channel input of NCSs is often required to have an infinite power for the optimal tracking problem in [2,10,13], which generally cannot be met in practice.…”
This paper focuses on the tracking performance limitation for a class of networked control systems(NCSs) with two-channel constraints. In communication channels, we consider bandwidth, energy constraints and additive colored Gaussian noise(ACGN) simultaneously. In plant, non-minimal zeros and unstable poles are considered; multi-repeated zeros and poles are also applicable. To obtain the optimal performance, the two-parameter controller is adopted. The theoretical results show that the optimal tracking performance is influenced by the non-minimum phase zeros, unstable poles, gain at all frequencies of the given plant, and the reference input signal for NCSs. Moreover, the performance limitation is also affected by the limited bandwidth, additive colored Gaussian noise, and the corresponding multiples for the non-minimum phase zeros and unstable poles. Additionally, the channel minimal input power constraints are given under the condition ensuring the stability of the system and acquiring system performance limitation. Finally, simulation examples are given to illustrate the theoretical results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
