The process of anesthesia is nonlinear with time delay and also there are some constraints which have to be considered in calculating administrative drug dosage. We present an Extended Kalman Filter (EKF) observer to estimate drug concentration in the patient's body and use this estimation in a state-space based Model of Predictive Controller (MPC) for controlling the depth of anesthesia. Bispectral Index (BIS) is used as a patient consciousness index and propofol as an anesthetic agent. Performance evaluations of the proposed controller, the results have been compared with those of a MPC controller. The results demonstrate that state-space MPC including the EKF estimator for controlling the anesthesia process can significantly increase the robustness in encountering patients' delay deviations in comparison with the MPC. . Keywords: Depth of Anesthesia (DOA); Patient's delay; PK-PD model; Model based Predictive Controller (MPC)
In this paper, a new filter called incremental predictive Kalman filter is developed and employed for the alignment of inertial navigation system using zero velocity updates method. Utilizing the incremental model error, a well posed cost function is presented for incremental predictive Kalman filter that leads to bias-free predictions. Besides, a weighted incremental term of past and present states is evident in the model error solution. This term, in conjunction with an integral action, has substantial effects on the robust performance of the alignment process against intense model uncertainty. Due to the horizon extension of the predictions and of the model errors to more than one-step ahead in the incremental predictive Kalman filter, this filter has a very flexible structure which enables it to implement the available references in the zero velocity updates method as a whole over its wide prediction horizon. The Monte Carlo simulations indicate that the alignment accuracy is noticeably affected due to use of the incremental predictive Kalman filter.
In this article, a robust non-linear recursive algorithm, featuring a highly reduced computational load, is proposed to estimate the thrust acceleration of a typical flight vehicle. The robustness of this new algorithm allows a significant increase of deviations in initial values of the estimated parameters. In the proposed algorithm, at first, a transformation of the nonlinear measurement equation to a linear one, without any approximation, is obtained. Then the recursive least squares algorithm is applied to the transformed equation. The maximum achievable accuracy of the estimation for the non-linear problem is obtained analytically by the Cramer-Rao lower bound and is compared with simulation results. Extensive simulations showed that the new method provides an unbiased as well as a more robust thrust acceleration estimate in comparison with the extended Kalman filter. Moreover, the proposed method is beneficial in that it has a lower number of parameters and results in a simple design with less computational effort.
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.