Optimal trajectory generation in ocean flows

In this paper it is shown that Lagrangian Coherent Structures (LCS) are useful in determining near optimal trajectories for autonomous underwater gliders in a dynamic ocean environment. This opens the opportunity for optimal path planning of autonomous underwater vehicles by studying the global flow geometry via dynamical systems methods. Optimal glider paths were computed for a 2-dimensional kinematic model of an end-point glider problem. Numerical solutions to the optimal control problem were obtained using Nonlinear Trajectory Generation (NTG) software. The resulting solution is compared to corresponding results on LCS obtained using the Direct Lyapunov Exponent method. The velocity data used for these computations was obtained from measurements taken in August, 2000, by HF-Radar stations located around Monterey Bay, CA

H2 Control with Time-Domain Constraints: Theory and an Application

In this paper, we study the problem of minimizing the 2 norm of a given transfer function subject to time-domain constraints on the time response of a different transfer function to a given test signal. The main result of this paper shows that this problem admits a minimizing solution in 2 . Moreover, rational solutions with performance arbitrarily close to optimal can be found by constructing families of approximating problems. Each one of these problems entails solving a finite-dimensional quadratic programming problem whose dimension can be determined before hand. These results are illustrated and experimentally validated by designing a controller for an active vision application

Control-Relevant Adaptive Personalized Modeling From Limited Clinical Data for Precise Warfarin Management

Warfarin is a challenging drug to administer due to the narrow therapeutic index of the International Normalized Ratio (INR), the inter- and intra-variability of patients, limited clinical data, genetics, and the effects of other medications. Goal: To predict the optimal warfarin dosage in the presence of the aforementioned challenges, we present an adaptive individualized modeling framework based on model (In)validation and semi-blind robust system identification. The model (In)validation technique adapts the identified individualized patient model according to the change in the patient's status to ensure the model's suitability for prediction and controller design. Results: To implement the proposed adaptive modeling framework, the clinical data of warfarin-INR of forty-four patients has been collected at the Robley Rex Veterans Administration Medical Center, Louisville. The proposed algorithm is compared with recursive ARX and ARMAX model identification methods. The results of identified models using one-step-ahead prediction and minimum mean squared analysis (MMSE) show that the proposed framework effectively predicts the warfarin dosage to keep the INR values within the desired range and adapt the individualized patient model to exhibit the true status of the patient throughout treatment. Conclusion: This paper proposes an adaptive personalized patient modeling framework from limited patientspecific clinical data. It is shown by rigorous simulations that the proposed framework can accurately predict a patient's doseresponse characteristics and it can alert the clinician whenever identified models are no longer suitable for prediction and adapt the model to the current status of the patient to reduce the prediction error

Low–Observable Nonlinear Trajectory Generation for Unmanned Air Vehicles”,

Abstract-The problem of finding a real time optimal trajectory to minimize the probability of detection (to maximize the probability of not-being-detected, pnd, function) of unmanned air vehicles by opponent radar detection systems is investigated. This paper extends our preliminary results on low observable trajectory generation in three ways. First, trajectory planning in the presence of detection by multiple radar systems, rather than single radar systems, is considered. Second, an overall probability of detection function is developed for the multiple radar case. In previous work, both probability of detection by a single radar and signature were developed in the theory section, but the examples used only signature constraints. In this work, the use of the overall probability of detection function is used, both because it aids in the extension to multiple radar systems and because it is a more direct measure of the desirable optimization criteria. The third extension is the use of updated signature and probability of detection models. The new models have a greater number of sharp gradients than the previous models, with low detectability regions for both a cone shaped areas centered around the nose as in the previous paper, as well as a cone-shaped area centered around rear of the air vehicle. The Nonlinear Trajectory Generation method (NTG), developed at Caltech, is used and motivated by the ability to provide real time solutions for constrained nonlinear optimization problems. Numerical simulations of multiple radar scenarios illustrate UAV trajectories optimized for both detectability and time