Asymptotic observability of distributed Boolean networks (DBNs) is studied in this article. Via a parallel extension method, asymptotic observability of the original system is converted to reachability at a fixed point of the extended system. Based on the structure matrix of the extended system, a necessary and sufficient condition is presented for asymptotic observability. Further, for unobservable systems, mode-dependent pinning control is first introduced and applied to achieve asymptotic observability, including the selections of pinning nodes, the design of output feedback controls, and the adding approaches. Then, a set of matrices is defined for the construction of the desired structure matrix. Based on it, a necessary condition is given to guarantee the solvability of the corresponding output feedback controls and the adding approaches. Finally, a numerical example is presented to show the effectiveness of the obtained results.
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http://dx.doi.org/10.1109/TCYB.2024.3355979 | DOI Listing |
Sensors (Basel)
January 2025
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
View Article and Find Full Text PDFISA Trans
December 2024
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China. Electronic address:
For Electro-Hydraulic Actuators (EHA) with parametric uncertainties and mismatched and matched disturbances, most existing robust adaptive control strategies can achieve only uniformly ultimately bounded tracking errors. An Extended-State-Observer (ESO) based asymptotic control scheme is proposed by incorporating the prescribed performance control into the backstepping framework to ensure satisfied tracking performance and anti-disturbance ability of EHA systems. A novel ESO is designed to acquire an asymptotic estimation without prior bounds of the mismatched disturbance and its derivatives.
View Article and Find Full Text PDFBiometrics
January 2025
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, United Kingdom.
Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
This paper investigates a consensus problem for a class of T-S fuzzy multiple-agent systems (MASs) with unknown input (UI). To begin with, an unknown input observer (UIO) is able to asymptotically estimate the system state and the UI is designed for each agent. In order to construct the UIO, the state interval estimation is obtained by first using zonotope theory.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Department of Electrical and Computer Engineering, American University of Beirut, P.O. Box 11-0236, Beirut 1107 2020, Lebanon.
The isotropic Cauchy distribution is a member of the central α-stable family that plays a role in the set of heavy-tailed distributions similar to that of the Gaussian density among finite second-moment laws. Given a sequence of observations, we are interested in characterizing the performance of Likelihood Ratio Tests, where two hypotheses are plausible for the observed quantities: either isotropic Cauchy or isotropic Gaussian. Under various setups, we show that the probability of error of such detectors is not always exponentially decaying with , with the leading term in the exponent shown to be logarithmic instead, and we determine the constants in that leading term.
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