Showing items related by title, author, creator and subject.
Trajectory Planning for Autonomous Underwater Vehicles in the Presence of Obstacles and a Nonlinear Flow Field using Mixed Integer Nonlinear ProgrammingWang, Tong; Lima, Ricardo; Giraldi, Loic; Knio, Omar (Computers & Operations Research, Elsevier BV, 2018-08-24) [Article]This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: 1) a novel approach to integrate the flow field into the MINLP model; 2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; 3) an algorithm that forces the solver to seek improved solutions; and 4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields.
Nonlinear partial least squares with Hellinger distance for nonlinear process monitoringHarrou, Fouzi; Madakyaru, Muddu; Sun, Ying (2016 IEEE Symposium Series on Computational Intelligence (SSCI), Institute of Electrical and Electronics Engineers (IEEE), 2017-02-16) [Conference Paper]This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
On the robustness of linear and non-linear fractional-order systems with non-linear uncertain parametersNdoye, Ibrahima; Darouach, Mohamed; Voos, Holger; Zasadzinski, Michel (IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION, Oxford University Press (OUP), 2016) [Article]This paper presents the robust stabilization problem of linear and non-linear fractional-order systems with non-linear uncertain parameters. The uncertainty in the model appears in the form of the combination of 'additive perturbation' and 'multiplicative perturbation'. Sufficient conditions for the robust asymptotical stabilization of linear fractional-order systems are presented in terms of linear matrix inequalities (LMIs) with the fractional-order 0 < α < 1. Sufficient conditions for the robust asymptotical stabilization of non-linear fractional-order systems are then derived using a generalization of the Gronwall-Bellman approach. Finally, a numerical example is given to illustrate the effectiveness of the proposed results.