This article considers the path-following problem for an unmanned aerial system (UAS), in which an online remote control station computes and sends control input signals to the vehicle over an adverse communication network. In that network configuration, the cyberattackers and malicious eavesdroppers are prone to erode the UAS's safety properties such as operational security and information privacy. To guarantee these properties, we introduce a secure model-predictive control (MPC) framework for achieving both optimal and safe path-following performance. The unique feature of this framework is that it can simultaneously address all the adversaries occurring in both remote station and network transmission links. Then, an encrypted MPC law is designed using an effective encoding scheme and the Paillier cryptography scheme. It is shown that the closed-loop stability can be guaranteed under the proposed MPC law. Simulation studies of UAS path following are conducted to validate the effectiveness of the proposed framework.

Patient blood pressure response to phenylephrine (PHP) drug infusion is modeled as intra-and-inter-variability model with time delay. This paper proposes a novel Bayesian-filtering-based approach for real-time estimating the model parameters variability and drug transport delay (time delay). From data-driven perspective, cross-correlation (XCF) of mean arterial pressure (MAP) measurement with drug infusion provides prior knowledge of time delay estimation, and cubature Kalman filter (CKF) supplies nonlinearity estimation of model parameters and posterior knowledge of time delay. The Bayesian-filtering-based approach was validated in sequential mechanism with animal experimental data. Real-time experimental test convinced the superiority of the proposed approach than multiple-model EKF (MMEKF) in both estimation accuracy of MAP and drug transport delay estimation and sensitivity analysis of model parameters and drug transportation delay estimation.

Efficient and intuitive interfacing of the interventionalist to the information and tools available from image-guided robotic assisted surgeries is required to achieve the full benefit of these technologies. Ongoing research has been performed into the use of forbidden region guided fixtures (FRVF) for human-in-the-loop control of image-guided procedures via haptic force-feedback devices (FFD). Although commercially available FFD provide sufficient degrees-of-freedom (DoF), collaborating clinicians, as well as the results of our previous work indicate that these systems are not completely intuitive for controlling fixed-point access interventional tool which have a remote center of motion. Within this context, we introduce a new FFD which is designed with the same DoF constraints as a fixed-point access interventional tool. The device is tested in a clinical simulation of a robot assisted trans-apical valve implantation under guidance from real-time magnetic resonance imaging. Pre-acquired real-time images are used in the clinical simulation to dynamically update the FRVF and therefore provide guiding forces to allow the operator to see the safe boundaries of operation via a visualization interface and physically feel them through the FFD. Inertial and gravity compensation and per DoF dynamic response of the physical prototype are validated and the frequency response of the system demonstrates it is adequate for tactile sensing. During clinical simulation the operator was successfully able to maneuver the tool within the safe path to the region of interest with the guidance of visual and force-feedback.

In this paper, we address the problem of designing parameter-dependent H/sup /spl infin// filters for output estimation in an LPV plant that includes known state delay. We assume that state-space data depend on parameters that are not known a priori but are measurable in real-time. We investigate the required conditions to satisfy both asymptotic stability and H/sup /spl infin// performance in terms of linear matrix inequalities (LMIs). The designed filters are looked for being capable of tracking the desired plant outputs in the presence of external disturbances. Two sets of LMI formulation are presented: one for a family of memoryless filters and another one for a class of filters which include state-delay in their dynamics. A simulation-based comparison is also provided in order to show the competence of our design methodology and superiority of the delayed filters relative to the memoryless ones.