This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. Fi...This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using v-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.展开更多
Underwater target signal detection faces great challenges due to complex ocean noise and time-varying signal characteristics.In this paper,a novel hybrid framework combining fractional attentional entropy(FAE)and enha...Underwater target signal detection faces great challenges due to complex ocean noise and time-varying signal characteristics.In this paper,a novel hybrid framework combining fractional attentional entropy(FAE)and enhanced intrinsic time scale decomposition(EITD)is proposed to improve the robustness of feature extraction.FAE quantifies the signal complexity through fractional-order attentional mechanism while EITD adaptively decomposes the non-stationary components.The extracted features are fed into a decision tree(DT)classifier for six types of ship target recognition,which achieves an average accuracy of 98.86%on a real-world dataset.Comparative experiments show that the proposed method is 19.05%superior to the traditional intrinsic time scale decomposition(ITD)and dispersion entropy(DispEn).In addition,implementation with probabilistic neural network(PNN)shows that DT achieves excellent generalization with limited training samples(98.86%vs.97.05%accuracy),and this research provides a theoretical basis for underwater weak target detection.展开更多
In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distort...In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.展开更多
文摘This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using v-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.
基金Authors gratefully acknowledge the supported by Natural Science Foundation of Shaanxi Province(2024JC-YBQN-0711)Natural Science Foundation of Shaanxi Provincial Department of Education(24JK0659).
文摘Underwater target signal detection faces great challenges due to complex ocean noise and time-varying signal characteristics.In this paper,a novel hybrid framework combining fractional attentional entropy(FAE)and enhanced intrinsic time scale decomposition(EITD)is proposed to improve the robustness of feature extraction.FAE quantifies the signal complexity through fractional-order attentional mechanism while EITD adaptively decomposes the non-stationary components.The extracted features are fed into a decision tree(DT)classifier for six types of ship target recognition,which achieves an average accuracy of 98.86%on a real-world dataset.Comparative experiments show that the proposed method is 19.05%superior to the traditional intrinsic time scale decomposition(ITD)and dispersion entropy(DispEn).In addition,implementation with probabilistic neural network(PNN)shows that DT achieves excellent generalization with limited training samples(98.86%vs.97.05%accuracy),and this research provides a theoretical basis for underwater weak target detection.
基金supported by the National Natural Science Foundation of China under Grant No.61671185 and 62071153.
文摘In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.
