State estimation algorithm deals with recovering some desired state variables of a dynamic system from available noisy measurements, and estimation of the state variables is one of the fundamental and significant problems in control and signal processing areas, and many significant progresses have been made in this area. In 1940s, Wiener, the founder of the modern statistical estimation theory,...

Abstract Regarding the low accuracy and instability of common online methods for estimating dynamic models in time domain, presence uncertainty system dynamics, sensor noise environmental disturbances, this area is still open further research. In paper, a new estimation method proposed based on robust meta‐heuristic adaptive LSQR (ORALSQR) simultaneous multi input/output linear model state vari...

In this study, an observer for estimating the state and unknown inputs is proposed monitoring anaerobic digestion processes. This estimator based on a dynamic model considering acidogenesis methanogenesis, consists of three sub-observers: (a) gramian-based fixed-time convergent inlet chemical oxygen demand (COD) acidogenic bacteria population, (b) asymptotic methanogenic (c) super-twisting syst...

Based on M-estimate, the problem of robust estimation fusion in decentralized architecture when the sensor noises are contaminated by outliers is considered. A simple robust Kalman filtering (RKF) scheme with weighted matrices of innovation sequences is introduced for local state estimation. Then, to avoid both the inconsistency of the Kalman filter and the performance conservation of the covar...

ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. therefore the special method should be considered. in this paper in to examine the effect of phenylephrine in rat corpus cavernosum, outlier robust nonlinear mixed estimation is used. in this study, eight different doses of phenylephrin...