Parametric Design for Proportional Plus Double Integral Controller with Applications to Tracking Control in Space Optical Communication

Tianyi Zhao,Guangren Duan 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.9

A complete parametric design approach is developed for a type of proportional plus double integral (P2I) controller. The proposed control law guarantees that the closed-loop system has the desired eigenstructure, and the output asymptotically tracks the ramp-type reference signal in the presence of ramp-type external disturbances. The proposed method is superior to the existing results in that it provides all degrees of freedom in design, which are further used to suppress the effects of other complicated time-varying disturbances and parameter perturbations by minimizing certain performance indices. The proposed method is applied to the high accuracy tracking control system design for satellite optical communication. Simulation results verify the superiority of the proposed method over the traditional proportional-integral (PI) controller.

Parametric Design for Observer-based P2I Controller with Applications to High-accuracy Tracking Control in Space Optical Communication

Tianyi Zhao,Guangren Duan 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.2

A parametric design approach is proposed for the proportional plus double integral (P2I) control law based on a reduced-order Luenberger state observer. Firstly, based on the Separation Principle of Eigenstructures proven in this paper, a complete parametric expression of the control law is obtained by parameterizing the statefeedback P2I control law and the reduced-order observer separately. The control law guarantees that the closed-loop system possesses desired eigenstructures, and the output asymptotically tracks a given ramp-type reference signal in the presence of ramp-type disturbances. All design degrees of freedom are obtained, which can be used to further achieve better performance by optimizing the parameters. Secondly, in order to enhance the robustness to other types of disturbances and parameter perturbations, two performance indices are parameterized, namely, the disturbance attenuation index and the eigenvalue sensitivity index, the latter is derived by using the obtained Separation Principle of Eigenvalue Sensitivities. Finally, the application to the high accuracy tracking control system design for satellite optical communication is presented, which well verifies the effect and the superiority of the proposed method. A parametric design approach is proposed for the proportional plus double integral (P2I) control law based on a reduced-order Luenberger state observer. Firstly, based on the Separation Principle of Eigenstructures proven in this paper, a complete parametric expression of the control law is obtained by parameterizing the statefeedback P2I control law and the reduced-order observer separately. The control law guarantees that the closed-loop system possesses desired eigenstructures, and the output asymptotically tracks a given ramp-type reference signal in the presence of ramp-type disturbances. All design degrees of freedom are obtained, which can be used to further achieve better performance by optimizing the parameters. Secondly, in order to enhance the robustness to other types of disturbances and parameter perturbations, two performance indices are parameterized, namely, the disturbance attenuation index and the eigenvalue sensitivity index, the latter is derived by using the obtained Separation Principle of Eigenvalue Sensitivities. Finally, the application to the high accuracy tracking control system design for satellite optical communication is presented, which well verifies the effect and the superiority of the proposed method.

A Mixed Co-clustering Algorithm Based on Information Bottleneck

Yongli Liu,Tianyi Duan,Xing Wan,Hao Chao 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.6

Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clusteringrelaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzyco-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy coclusteringand possibilistic clustering, and formulates an objective function which includes a distance functionthat employs information bottleneck theory to measure the distance between feature data point and featurecluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimentalresults show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM,RFCC and FCCI, in terms of accuracy and robustness.

A Mixed Co-clustering Algorithm Based on Information Bottleneck

Liu, Yongli,Duan, Tianyi,Wan, Xing,Chao, Hao Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.6

Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy co-clustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.

Complete Parametric Solutions to the Fundamental Problem in High-order Fully Actuated System Approach

Guang-Ren Duan,Qin Zhao,Tianyi Zhao 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.1

The high-order fully actuated system (HOFAS) approach has recently been proposed, aiming at establishing a unified architecture for control of general nonlinear systems. Its core idea is to firstly obtain a HOFAS model for a dynamical system, and then to cancel the nonlinearity using the full-actuation property. Based on this, the control problem of both linear and many types of nonlinear systems is finally turned into a specific eigenstructure assignment problem of a particular matrix pair. Because of this, the specific eigenstructure assignment problem is considered as the fundamental problem of the HOFAS approach, and is investigated in detail in this paper. A general parametric solution is established in an iterative form with all the degrees of freedom provided, and special solutions for some commonly used cases are also given. These form a database for various design problems and provide some ready-to-use results. Finally, illustrative examples demonstrate the usage of the database.

Incremental Fuzzy Clustering Based on a Fuzzy Scatter Matrix

Liu, Yongli,Wang, Hengda,Duan, Tianyi,Chen, Jingli,Chao, Hao Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

Incremental fuzzy clustering based on a fuzzy scatter matrix

Yongli Liu,Hengda Wang,Tianyi Duan,Jingli Chen,Hao Chao 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithmsare very popular. Usually, these algorithms only concern the within-cluster compactness and ignore thebetween-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS)clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-meansalgorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, sothat they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweenclustermatrix simultaneously to obtain the minimum within-cluster distance and maximum between-clusterdistance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experimentson some artificial datasets and real datasets separately. And experimental results show that, compared withSPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.