Relevant bibliographies by topics / Nonlinear robust optimization

Academic literature on the topic 'Nonlinear robust optimization'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Nonlinear robust optimization"

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Leyffer, Sven, Matt Menickelly, Todd Munson, Charlie Vanaret, and Stefan M. Wild. "A survey of nonlinear robust optimization." INFOR: Information Systems and Operational Research 58, no. 2 (March 17, 2020): 342–73. http://dx.doi.org/10.1080/03155986.2020.1730676.

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Yuan, Yuan, Zukui Li, and Biao Huang. "Nonlinear robust optimization for process design." AIChE Journal 64, no. 2 (September 12, 2017): 481–94. http://dx.doi.org/10.1002/aic.15950.

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Zhang, Y. "General Robust-Optimization Formulation for Nonlinear Programming." Journal of Optimization Theory and Applications 132, no. 1 (February 16, 2007): 111–24. http://dx.doi.org/10.1007/s10957-006-9082-z.

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Diehl, Moritz, Hans Georg Bock, and Ekaterina Kostina. "An approximation technique for robust nonlinear optimization." Mathematical Programming 107, no. 1-2 (December 30, 2005): 213–30. http://dx.doi.org/10.1007/s10107-005-0685-1.

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Ben-Tal, Aharon, Ruud Brekelmans, Dick den Hertog, and Jean-Philippe Vial. "Globalized Robust Optimization for Nonlinear Uncertain Inequalities." INFORMS Journal on Computing 29, no. 2 (May 2017): 350–66. http://dx.doi.org/10.1287/ijoc.2016.0735.

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HEYNE, GREGOR, MICHAEL KUPPER, and LUDOVIC TANGPI. "PORTFOLIO OPTIMIZATION UNDER NONLINEAR UTILITY." International Journal of Theoretical and Applied Finance 19, no. 05 (July 29, 2016): 1650029. http://dx.doi.org/10.1142/s0219024916500291.

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This paper studies the utility maximization problem of an agent with nontrivial endowment, and whose preferences are modeled by the maximal subsolution of a backward stochastic differential equation (BSDE). We prove existence of an optimal trading strategy and relate our existence result to the existence of a maximal subsolution to a controlled decoupled forward–BSDE (FBSDE). Using BSDE duality, we show that the utility maximization problem can be seen as a robust control problem admitting a saddle point if the generator of the BSDE additionally satisfies a specific growth condition. We show by convex duality that any saddle point of the robust control problem agrees with a primal and a dual optimizer of the utility maximization problem, and can be characterized in terms of a BSDE solution.
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OISHI, Yasuaki. "501 Robust Control against Nonlinear Uncertainty : An Approach through Robust Optimization." Proceedings of Conference of Tokai Branch 2008.57 (2008): 349–50. http://dx.doi.org/10.1299/jsmetokai.2008.57.349.

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Ma, Bai, Wang, and Fang. "Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization." Sensors 19, no. 17 (August 29, 2019): 3747. http://dx.doi.org/10.3390/s19173747.

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The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.
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JAYARAMAN, BASKAR, and BRADLEY R. HOLT. "An optimization approach to robust nonlinear control design." International Journal of Control 59, no. 3 (March 1994): 639–64. http://dx.doi.org/10.1080/00207179408923098.

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Lee, Chang Jun, Vinay Prasad, and Jong Min Lee. "Stochastic Nonlinear Optimization for Robust Design of Catalysts." Industrial & Engineering Chemistry Research 50, no. 7 (April 6, 2011): 3938–46. http://dx.doi.org/10.1021/ie102103w.

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Dissertations / Theses on the topic "Nonlinear robust optimization"

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Zhao, Yong. "Nonlinear compensation and heterogeneous data modeling for robust speech recognition." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47566.

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The goal of robust speech recognition is to maintain satisfactory recognition accuracy under mismatched operating conditions. This dissertation addresses the robustness issue from two directions. In the first part of the dissertation, we propose the Gauss-Newton method as a unified approach to estimating noise parameters for use in prevalent nonlinear compensation models, such as vector Taylor series (VTS), data-driven parallel model combination (DPMC), and unscented transform (UT), for noise-robust speech recognition. While iterative estimation of noise means in a generalized EM framework has been widely known, we demonstrate that such approaches are variants of the Gauss-Newton method. Furthermore, we propose a novel noise variance estimation algorithm that is consistent with the Gauss-Newton principle. The formulation of the Gauss-Newton method reduces the noise estimation problem to determining the Jacobians of the corrupted speech parameters. For sampling-based compensations, we present two methods, sample Jacobian average (SJA) and cross-covariance (XCOV), to evaluate these Jacobians. The Gauss-Newton method is closely related to another noise estimation approach, which views the model compensation from a generative perspective, giving rise to an EM-based algorithm analogous to the ML estimation for factor analysis (EM-FA). We demonstrate a close connection between these two approaches: they belong to the family of gradient-based methods except with different convergence rates. Note that the convergence property can be crucial to the noise estimation in many applications where model compensation may have to be frequently carried out in changing noisy environments to retain desired performance. Furthermore, several techniques are explored to further improve the nonlinear compensation approaches. To overcome the demand of the clean speech data for training acoustic models, we integrate nonlinear compensation with adaptive training. We also investigate the fast VTS compensation to improve the noise estimation efficiency, and combine the VTS compensation with acoustic echo cancellation (AEC) to mitigate issues due to interfering background speech. The proposed noise estimation algorithm is evaluated for various compensation models on two tasks. The first is to fit a GMM model to artificially corrupted samples, the second is to perform speech recognition on the Aurora 2 database, and the third is on a speech corpus simulating the meeting of multiple competing speakers. The significant performance improvements confirm the efficacy of the Gauss-Newton method to estimating the noise parameters of the nonlinear compensation models. The second research work is devoted to developing more effective models to take full advantage of heterogeneous speech data, which are typically collected from thousands of speakers in various environments via different transducers. The proposed synchronous HMM, in contrast to the conventional HMMs, introduces an additional layer of substates between the HMM state and the Gaussian component variables. The substates have the capability to register long-span non-phonetic attributes, such as gender, speaker identity, and environmental condition, which are integrally called speech scenes in this study. The hierarchical modeling scheme allows an accurate description of probability distribution of speech units in different speech scenes. To address the data sparsity problem in estimating parameters of multiple speech scene sub-models, a decision-based clustering algorithm is presented to determine the set of speech scenes and to tie the substate parameters, allowing us to achieve an excellent balance between modeling accuracy and robustness. In addition, by exploiting the synchronous relationship among the speech scene sub-models, we propose the multiplex Viterbi algorithm to efficiently decode the synchronous HMM within a search space of the same size as for the standard HMM. The multiplex Viterbi can also be generalized to decode an ensemble of isomorphic HMM sets, a problem often arising in the multi-model systems. The experiments on the Aurora 2 task show that the synchronous HMMs produce a significant improvement in recognition performance over the HMM baseline at the expense of a moderate increase in the memory requirement and computational complexity.
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Aylward, Erin M. "Robust stability and contraction analysis of nonlinear systems via semidefinite optimization." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37850.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 107-110).
A wide variety of stability and performance problems for linear and certain classes of nonlinear dynamical systems can be formulated as convex optimization problems involving linear matrix inequalities (LMIs). These formulations can be solved numerically with computationally-effcient interior-point methods. Many of the first LMI-based stability formulations applied to linear systems and the class of nonlinear systems representable as an interconnection of a linear system with bounded uncertainty blocks. Recently, stability and performance analyses of more general nonlinear deterministic systems, namely those with polynomial or rational dynamics, have been converted into an LMI framework using sum of squares (SOS) programming. SOS programming combines elements of computational algebra and convex optimization to provide e±cient convex relaxations for various computationally-hard problems. In this thesis we extend the class of systems that can be analyzed with LMI-based methods.
(cont.) We show how to analyze the robust stability properties of uncertain non-linear systems with polynomial or rational dynamics, as well as a class of systems with external inputs, via contraction analysis and SOS programming. Specifically, we show how contraction analysis, a stability theory for nonlinear dynamical systems in which stability is designed incrementally between two arbitrary trajectories via a contraction metric, provides a useful framework for analyzing the stability of uncertain systems. Then, using SOS programming we develop an algorithmic method to search for contraction metrics for these systems. The search process is made computationally tractable by relaxing matrix deniteness constraints, the feasibility of which indicates the existence of a contraction metric, to SOS constraints on polynomial matrices. We illustrate our results through examples from the literature and show how our contraction-based approach offers advantages when compared with traditional Lyapunov analysis.
by Erin M. Aylward.
S.M.
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Zhao, Yiming. "Efficient and robust aircraft landing trajectory optimization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43586.

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This thesis addresses the challenges in the efficient and robust generation and optimization of three-dimensional landing trajectories for fixed-wing aircraft subject to prescribed boundary conditions and constraints on maneuverability and collision avoidance. In particular, this thesis focuses on the airliner emergency landing scenario and the minimization of landing time. The main contribution of the thesis is two-fold. First, it provides a hierarchical scheme for integrating the complementary strength of a variety of methods in path planning and trajectory optimization for the improvement in efficiency and robustness of the overall landing trajectory optimization algorithm. The second contribution is the development of new techniques and results in mesh refinement for numerical optimal control, optimal path tracking, and smooth path generation, which are all integrated in a hierarchical scheme and applied to the landing trajectory optimization problem. A density function based grid generation method is developed for the mesh refinement process during numerical optimal control. A numerical algorithm is developed based on this technique for solving general optimal control problems, and is used for optimizing aircraft landing trajectories. A path smoothing technique is proposed for recovering feasibility of the path and improving the tracking performance by modifying the path geometry. The optimal aircraft path tracking problem is studied and analytical results are presented for both the minimum-time, and minimum-energy tracking with fixed time of arrival. The path smoothing and optimal path tracking methods work together with the geometric path planner to provide a set of feasible initial guess to the numerical optimal control algorithm. The trajectory optimization algorithm in this thesis was tested by simulation experiments using flight data from two previous airliner accidents under emergency landing scenarios.The real-time application of the landing trajectory optimization algorithm as part of the aircraft on-board automation avionics system has the potential to provide effective guidelines to the pilots for improving the fuel consumption during normal landing process, and help enhancing flight safety under emergency landing scenarios. The proposed algorithms can also help design optimal take-off and landing trajectories and procedures for airports.
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Tandon, Bhawna [Verfasser]. "How Can Robust Control of Nonlinear Systems be Achieved? Examining Optimization Techniques / Bhawna Tandon." München : GRIN Verlag, 2019. http://d-nb.info/1197385347/34.

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Sünderhauf, Niko. "Robust optimization for simultaneous localization and mapping." Thesis, Technischen Universitat Chemnitz, 2012. https://eprints.qut.edu.au/109667/1/109667.pdf.

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers . In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem’s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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Sünderhauf, Niko. "Robust Optimization for Simultaneous Localization and Mapping." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86443.

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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Andreas, April Kramer. "Mathematical Programming Algorithms for Reliable Routing and Robust Evacuation Problems." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/195737.

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Most traditional routing problems assume perfect operability of all arcs and nodes. However, when independent arc failure probabilities exist, a secondary objective must be present to retain some measure of expected functionality. We first briefly consider the reliability-constrained single-path problem, where we look for the lowest cost path that meets a reliability side constraint. This analysis enables us to then examine the reliability-constrained two-path problem, which seeks to establish two minimum-cost paths between a source and destination node wherein at least one path must remain fully operable with some threshold probability. We consider the case in which both paths must be arc-disjoint and the case in which arcs can be shared between the paths. We prove both problems to be NP-hard. We examine strategies for solving the resulting nonlinear integer program, including pruning, coefficient tightening, lifting, and branch-and-bound partitioning schemes. Next, we consider the reliable h-path routing problem, which seeks a minimum-cost set of h ≥ 2 arc-independent paths between a source and destination node, such that the probability that at least one path remains operational is sufficiently large. Our prior arc-based models and algorithms tailored for the case in which h = 2 do not extend well to the general h-path problem. Thus, we propose two alternative integer programming formulations for the h-path problem in which the variables correspond to origin-destination paths. We propose two branch-and-price-and-cut algorithms for solving these new formulations, and provide computational results to demonstrate the efficiency of these algorithms. Finally, we examine the robust design of an evacuation tree, in which evacuation is subject to capacity restrictions on arcs. Given a discrete set of disaster scenarios with varying network populations, arc capacities, transit times, and time-dependent penalty functions, we seek to establish an optimal a priori evacuation tree that minimizes the expected evacuation penalty. The solution strategy is based on Benders decomposition, and we provide effcient methods for obtaining primal and dual sub-problem solutions. We analyze techniques for strengthening the master problem formulation, thus reducing the number of master problem solutions required for the algorithm's convergence.
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Penet, Maxime. "Robust Nonlinear Model Predictive Control based on Constrained Saddle Point Optimization : Stability Analysis and Application to Type 1 Diabetes." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00968899.

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This thesis deals with the design of a robust and safe control algorithm to aim at an artificial pancreas. More precisely we will be interested in controlling the stabilizing part of a classical cure. To meet this objective, the design of a robust nonlinear model predictive controller based on the solution of a saddle point optimization problem is considered. Also, to test the controller performances in a realistic case, numerical simulations on a FDA validated testing platform are envisaged.In a first part, we present an extension of the usual nonlinear model predictive controller designed to robustly control, in a sampled-data framework, systems described by nonlinear ordinary differential equations. This controller, which computes the best control input by considering the solution of a constrained saddle point optimization problem, is called saddle point model predictive controller (SPMPC). Using this controller, it is proved that the closed-loop is Ultimately Bounded and, with some assumptions on the problem structure, Input-to State practically Stable. Then, we are interested in numerically solving the corresponding control problem. To do so, we propose an algorithm inspired from the augmented Lagrangian technique and which makes use of adjoint model.In a second part, we consider the application of this controller to the problem of artificial blood glucose control. After a modeling phase, two models are retained. A simple one will be used to design the controller and a complex one will be used to simulate realistic virtual patients. This latter is needed to validate our control approach. In order to compute a good control input, the SPMPC controller needs the full state value. However, the sensors can only provide the value of blood glucose. That is why the design of an adequate observer is envisaged. Then, numerical simulations are performed. The results show the interest of the approach. For all virtual patients, no hypoglycemia event occurs and the time spent in hyperglycemia is too short to induce damageable consequences. Finally, the interest of extending the SPMPC approach to consider the control of time delay systems in a sampled-data framework is numerically explored.
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Nielsen, Jerel Bendt. "Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7584.

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With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.
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Köbis, Elisabeth Anna Sophia [Verfasser], Christiane [Akademischer Betreuer] Tammer, and Akhtar [Akademischer Betreuer] Khan. "On robust optimization : a unified approach to robustness using a nonlinear scalarizing functional and relations to set optimization / Elisabeth Anna Sophia Köbis. Betreuer: Christiane Tammer ; Akhtar Khan." Halle, Saale : Universitäts- und Landesbibliothek Sachsen-Anhalt, 2014. http://d-nb.info/1053326432/34.

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Book chapters on the topic "Nonlinear robust optimization"

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Andrei, Neculai. "Robust Stability Analysis." In Nonlinear Optimization Applications Using the GAMS Technology, 273–86. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6797-7_11.

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Kabziński, J. "Robust stabilization of nonlinear systems by optimal controllers." In System Modelling and Optimization, 91–98. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34897-1_8.

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Morariu, Vlad I., Octavia I. Camps, Mario Sznaier, and Hwasup Lim. "Robust Cooperative Visual Tracking: A Combined NonLinear Dimensionality Reduction/Robust Identification Approach." In Advances in Cooperative Control and Optimization, 353–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74356-9_22.

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Duo, Jingyun, Lei Ji, and Long Zhao. "Robust Monocular Visual-Inertial SLAM Using Nonlinear Optimization." In Lecture Notes in Electrical Engineering, 560–68. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8450-3_59.

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Haas, Lukas, and Rolf Steinbuch. "Robust and Reliable Bionic Optimization of Nonlinear Control Problems." In Advances in Intelligent Systems and Computing, 165–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53480-0_17.

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Coutinho, Daniel F., and Alain Vande Wouwer. "A Robust H∞ Quasi-LPV Approach for Designing Nonlinear Observers." In Recent Advances in Optimization and its Applications in Engineering, 21–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12598-0_2.

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Rajendran, Manivasagam, P. Parthasarathy, and R. Anbumozhi. "Robust Analysis of T-S Fuzzy Controller for Nonlinear System Using H-Infinity." In Advanced Engineering Optimization Through Intelligent Techniques, 643–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8196-6_56.

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Liu, Shirong, Simon X. Yang, and Jinshou Yu. "Robust Modeling for Nonlinear Dynamic Systems Using a Neurofuzzy Approach with Iterative Optimization." In Advances in Neural Networks – ISNN 2005, 418–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_68.

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Kumar, Jitendra, Vishal Goyal, and Devbrat Gupta. "Reliable Incorporation of Robust Fractional-Order Nonlinear PID Controller into Integrated Power System Using Gray Wolf Optimization Method." In Advances in Data and Information Sciences, 333–43. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5292-0_31.

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Marcos, Maria da Graça, J. A. Tenreiro Machado, and T. P. Azevedo-Perdicoúlis. "Evolutionary Trajectory Optimization for Redundant Robots." In Nonlinear Science and Complexity, 347–53. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9884-9_40.

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Conference papers on the topic "Nonlinear robust optimization"

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Li, Qingfeng, Hao Gu, Cuihong Han, Weimeng Gong, Shuang Song, and Max Q. H. Meng. "Robust visual inertial monocular using nonlinear optimization." In 2017 IEEE International Conference on Information and Automation (ICIA). IEEE, 2017. http://dx.doi.org/10.1109/icinfa.2017.8078956.

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Ly, Uy-Loi. "Robust Control Design using Nonlinear Constrained Optimization." In 1990 American Control Conference. IEEE, 1990. http://dx.doi.org/10.23919/acc.1990.4790879.

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"NONLINEAR METAMODELING AND ROBUST OPTIMIZATION IN AUTOMOTIVE DESIGN." In Special Session on Computationally Efficient Simulation-Driven Engineering Design Optimization and Modeling. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003646704830491.

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Bai, Xiaoli, James Turner, and John Junkins. "A Robust Homotopy Method for Equality Constrained Nonlinear Optimization." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5845.

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Megretski, Alexandre. "Convex optimization in robust identification of nonlinear feedback." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739470.

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McNaughton, Matthew. "CASTRO: robust nonlinear trajectory optimization using multiple models." In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iros.2007.4399046.

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Song, Junguo. "Decentralized Robust Control of Nonlinear Coupled Tanks Systems." In 2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2022. http://dx.doi.org/10.1109/docs55193.2022.9967777.

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"Sigmapoint Approach for Robust Optimization of Nonlinear Dynamic Systems." In 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004026401990207.

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Ramakrishnan, Balaji, and S. S. Rao. "A Robust Optimization Approach Using Taguchi’s Loss Function for Solving Nonlinear Optimization Problems." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0093.

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Abstract The application of the concept of robust design, based on Taguchi’s loss function, in formulating and solving nonlinear optimization problems is investigated. The effectiveness of the approach is illustrated with two examples. The first example is a machining parameter optimization problem wherein the production cost, tool life and production rate are optimized with limitations on machining characteristics such as cutting power, cutting tool temperature and surface finish. The second example is a welded beam design problem where the dimensions of the weldment and the beam are found without exceeding the limitations stated on the shear stress in the weld, normal stress in the beam, buckling load on the beam and tip deflection of the beam. The results are highlighted by comparing the solutions of the robust formulation with those obtained from the conventional formulation. The methodology presented in this work is expected to be useful in the design of products and processes which are least sensitive to the noises and which reflect in higher quality.
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Guo, Bicheng, and Jiang Guo. "Robust global fast terminal sliding mode control for nonlinear hydraulic turbine governing system." In Control and Optimization of Renewable Energy Systems. Calgary,AB,Canada: ACTAPRESS, 2019. http://dx.doi.org/10.2316/p.2019.859-002.

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Reports on the topic "Nonlinear robust optimization"

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Teel, Andrew R. Optimization-Based Robust Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada452020.

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