Relevant bibliographies by topics / Hierarchical quadratic programming

Academic literature on the topic 'Hierarchical quadratic programming'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Hierarchical quadratic programming"

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Kumar, Suchet, and Madhuchanda Rakshit. "A Solution of Fuzzy Multilevel Quadratic Fractional Programming Problem through Interactive Fuzzy Goal Programming Approach." International Journal of Fuzzy Mathematical Archive 13, no. 01 (2017): 83–97. http://dx.doi.org/10.22457/ijfma.v13n1a9.

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The purpose of this paper is to study the fuzzy multilevel quadratic fractional programming problem through fuzzy goal programming procedure. A fuzzy multilevel quadratic fractional programming problem is a type of hierarchical programming problem which contains fuzzy parameters as coefficients of cost in objective function, the resources and the technological coefficients. Here, we are considering those fuzzy parameters as the triangular fuzzy numbers. Firstly, we are transferring the fuzzy multilevel quadratic fractional programming problem into a deterministic multilevel multiobjective quadratic fractional programming problem by using Zadeh extension principle. Then, an interactive fuzzy goal programming procedure is used to solve this equivalent deterministic multiobjective multilevel quadratic fractional programming problem by using respective membership functions. An illustrative numerical example for fuzzy four level quadratic fractional programming problem is provided to reveal the practicability of the proposed method.
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Pérez-Villeda, Héctor M., Gustavo Arechavaleta, and América Morales-Díaz. "Multi-vehicle coordination based on hierarchical quadratic programming." Control Engineering Practice 94 (January 2020): 104206. http://dx.doi.org/10.1016/j.conengprac.2019.104206.

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Escande, Adrien, Nicolas Mansard, and Pierre-Brice Wieber. "Hierarchical quadratic programming: Fast online humanoid-robot motion generation." International Journal of Robotics Research 33, no. 7 (May 2014): 1006–28. http://dx.doi.org/10.1177/0278364914521306.

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Shi, Xuanyang, Junyao Gao, Yizhou Lu, Dingkui Tian, and Yi Liu. "Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming." Sensors 21, no. 5 (March 2, 2021): 1696. http://dx.doi.org/10.3390/s21051696.

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The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback controller to adjust the landing point of a robot leg is inaccurate in the presence of disturbances and a nonlinear optimization method with multiple variables is complicated and thus unsuitable for real-time control. In this paper, to achieve real-time optimization, a CoM-velocity feedback controller is used to calculate the virtual landing point. We construct a touchdown return map based on a virtual landing point and use nonlinear least squares to optimize spring stiffness. For robot whole-body control, hierarchical quadratic programming optimization is used to achieve strict task priority. The dynamic equation is given the highest priority and inverse dynamics are directly used to solve it, reducing the number of optimizations. Simulation and experimental results show that a force-controlled biped robot with the proposed method can stably walk on unknown uneven ground with a maximum obstacle height of 5 cm. The robot can recover from a 5 Nm disturbance during walking without falling.
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Koung, Daravuth, Olivier Kermorgant, Isabelle Fantoni, and Lamia Belouaer. "Cooperative Multi-Robot Object Transportation System Based on Hierarchical Quadratic Programming." IEEE Robotics and Automation Letters 6, no. 4 (October 2021): 6466–72. http://dx.doi.org/10.1109/lra.2021.3092305.

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Kim, Sanghyun, Keunwoo Jang, Suhan Park, Yisoo Lee, Sang Yup Lee, and Jaeheung Park. "Continuous Task Transition Approach for Robot Controller Based on Hierarchical Quadratic Programming." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 1603–10. http://dx.doi.org/10.1109/lra.2019.2896769.

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Tian, Dingkui, Junyao Gao, Xuanyang Shi, Yizhou Lu, and Chuzhao Liu. "Vertical Jumping for Legged Robot Based on Quadratic Programming." Sensors 21, no. 11 (May 25, 2021): 3679. http://dx.doi.org/10.3390/s21113679.

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The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot’s posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot’s posture to be near a desired constant value in the flight phase. The controller is tested in two simulation environments with and without the flight phase controller, the results validate the flight phase controller, with the HQP controller having a maximum error of the CoM in the x direction and y direction of 0.47 and 0.82 cm and thus enabling the strict prioritization of the CoM.
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Abohany, A. A., Rizk Masoud Rizk-Allah, Diana T. Mosa, and Aboul Ella Hassanien. "A Novel Approach for Solving a Fully Rough Multi-Level Quadratic Programming Problem and Its Application." International Journal of Service Science, Management, Engineering, and Technology 11, no. 4 (October 2020): 137–65. http://dx.doi.org/10.4018/ijssmet.2020100109.

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The most widely used actions and decisions of the real-world tasks frequently appear as hierarchical systems. To deal with these systems, the multi-level programming problem presents the most flourished technique. However, practical situations involve some the impreciseness regarding some decisions and performances; RST provides a vital role by considering the lower and upper bounds of any aspect of uncertain decision. By preserving the advantages of it, in the present study, solving fully rough multi-level quadratic programming problems over the variables, parameters of the objective functions, and the constraints such as rough intervals are focused on. The proposed approach incorporates the interval method, slice-sum method, Frank and Wolfe algorithm, and the decomposition algorithm to reach optimal values as rough intervals. The proposed is validated by an illustrative example, and also environmental-economic power dispatch is investigated as a real application. Finally, the proposed approach is capable of handling the fully rough multi-level quadratic programming models.
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Li, Gang, Hai Lan Han, Chao Wang, and Gao Feng Ma. "Study on Fuzzy PI Control of Vehicle Yaw Moment Based on Optimal Allocation of Braking Forces." Applied Mechanics and Materials 556-562 (May 2014): 2293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2293.

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For vehicle direct yaw moment control (DYC) ,the additional yaw moment decision method based on the fuzzy PI control and optimal allocation method of yaw moment based on quadratic programming are studied. Yaw moment control adopts hierarchical control method.The fuzzy PI controller and brake force optimization distributor are designed. The control method is verified through the Matlab/Simulink and CarSim co-simulation experiment.The results show that the control method can make the vehicle track the expected value better and improve the driving stability effectively.
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Wang, Pengcheng, Weile Xu, Hao Zhu, Hui Tian, and Guobiao Cai. "An Application of Analytical Target Cascading for a Hierarchical Multidisciplinary System: The Preliminary Design of a Launch Vehicle Powered by Hybrid Rocket Motors." Aerospace 9, no. 12 (December 1, 2022): 778. http://dx.doi.org/10.3390/aerospace9120778.

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Analytical target cascading (ATC) is a method for coordinating hierarchical system design optimization with a decomposition-based framework. Since a launch vehicle (LV) is usually powered by two or more stages of rocket motors, the overall design of the LV clearly has a hierarchical structure, including system level (conducted by the general design department) and subsystem level (conducted by the motor stage design department). In particular, the subsystem level contains stage-divided elements rather than discipline-divided elements. Therefore, ATC is inherently suitable for the overall design of the LV. This paper presents an ATC decomposition framework for LV design according to practical engineering. The feasibility of the multi-island genetic algorithm (MIGA) used in the ATC decomposition is verified by a mathematical programming test, in which non-linear programming with the quadratic Lagrangian (NLPQL) algorithm is set as a comparison. The multi-disciplinary analysis modules of a hybrid rocket motor (HRM) propelled LV, including propulsion, structure, aerodynamics and trajectory, are established. A hierarchical decomposition is proposed for this multi-level design with a multi-disciplinary model. The application and optimization results verify the feasibility of the ATC decomposition framework with MIGA in the preliminary design of the LV and the final orbit accuracy is better than that of the MDF method. In addition, the final design schemes also prove that HRMs can be considered as a feasible choice of propulsion system for a small payload at low earth orbit.
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Dissertations / Theses on the topic "Hierarchical quadratic programming"

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Johansson, Marcus. "Online Whole-Body Control using Hierarchical Quadratic Programming : Implementation and Evaluation of the HiQP Control Framework." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133224.

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The application of local optimal control is a promising paradigm for manipulative robot motion generation.In practice this involves instantaneous formulations of convex optimization problems depending on the current joint configuration of the robot and the environment.To be effective, however, constraints have to be carefully constructed as this kind of motion generation approach has a trade-off of completeness.Local optimal solvers, which are greedy in a temporal sense, have proven to be significantly more effective computationally than classical grid-based or sampling-based planning approaches. In this thesis we investigate how a local optimal control approach, namely the task function approach, can be implemented to grant high usability, extendibility and effectivity.This has resulted in the HiQP control framework, which is compatible with ROS, written in C++.The framework supports geometric primitives to aid in task customization by the user.It is also modular as to what communication system it is being used with, and to what optimization library it uses for finding optimal controls. We have evaluated the software quality of the framework according to common quantitative methods found in the literature.We have also evaluated an approach to perform tasks using minimal jerk motion generation with promising results.The framework also provides simple translation and rotation tasks based on six rudimentary geometric primitives.Also, task definitions for specific joint position setting, and velocity limitations were implemented.
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Koung, Daravuth. "Cooperative navigation of a fleet of mobile robots." Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0044.

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L’intérêt pour l’intégration des systèmes multi-robots (MRS) dans les applications du monde réel augmente de plus en plus, notamment pour l’exécution de tâches complexes. Pour les tâches de transport de charges, différentes stratégies de manutention de charges ont été proposées telles que : la poussée seule, la mise en cage et la préhension. Dans cette thèse, nous souhaitons utiliser une stratégie de manipulation simple : placer l’objet à transporter au sommet d’un groupe de robots mobiles. Ainsi, cela nécessite un contrôle de formation rigide. Nous proposons deux algorithmes de formation. L’algorithme de consensus est l’un d’entre eux. Nous adaptons un contrôleur de flocking dynamique pour qu’il soit utilisé dans le système à un seul intégrateur, et nous proposons un système d’évitement d’obstacles qui peut empêcher le fractionnement tout en évitant les obstacles. Le deuxième contrôle de formation est basé sur l’optimisation quadratique hiérarchique (HQP). Le problème est décomposé en plusieurs objectifs de tâches : formation, navigation,évitement d’obstacles et limites de vitesse. Ces tâches sont représentées par des contraintes d’égalité et d’inégalité avec différentsniveaux de priorité, qui sont résolues séquentiellement par le HQP. Enfin, une étude sur les algorithmes d’allocation des tâches(Contract Net Protocol et Tabu Search) est menée afin de déterminer une solution appropriée pour l’allocation des tâches dans l’environnementindustriel
The interest in integrating multirobot systems (MRS) into real-world applications is increasing more and more, especially for performing complex tasks. For loadcarrying tasks, various load-handling strategies have been proposed such as: pushingonly, caging, and grasping. In this thesis, we aim to use a simple handling strategy: placing the carrying object on top of a group of wheeled mobile robots. Thus, it requires a rigid formation control. A consensus algorithm is one of the two formation controllers we apply to the system. We adapt a dynamic flocking controller to be used in the singleintegrator system, and we propose an obstacle avoidance that can prevent splitting while evading the obstacles. The second formation control is based on hierarchical quadratic programming (HQP). The problem is decomposed into multiple task objectives: formation, navigation, obstacle avoidance, velocity limits. These tasks are represented by equality and inequality constraints with different levels of priority, which are solved sequentially by the HQP. Lastly, a study on task allocation algorithms (Contract Net Protocol and Tabu Search) is carried out in order to determine an appropriate solution for allocating tasks in the industrial environment
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Conference papers on the topic "Hierarchical quadratic programming"

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Tassi, Francesco, Elena De Momi, and Arash Ajoudani. "Augmented Hierarchical Quadratic Programming for Adaptive Compliance Robot Control." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561506.

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Tassi, Francesco, Soheil Gholami, Simone Giudice, and Arash Ajoudani. "Impact Planning and Pre-configuration based on Hierarchical Quadratic Programming." In 2022 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2022. http://dx.doi.org/10.1109/icra46639.2022.9811681.

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Lutscher, Ewald, and Gordon Cheng. "Hierarchical inequality task specification for indirect force controlled robots using quadratic programming." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6943234.

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Kim, Myeong-Ju, Daegyu Lim, Gyeongjae Park, and Jaeheung Park. "Humanoid Balance Control using Centroidal Angular Momentum based on Hierarchical Quadratic Programming." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981036.

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Tassi, Francesco, Francesco Iodice, Elena De Momi, and Arash Ajoudani. "Sociable and Ergonomic Human-Robot Collaboration through Action Recognition and Augmented Hierarchical Quadratic Programming." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9982160.

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Kim, Sanghyun, Keunwoo Jang, Suhan Park, Yisoo Lee, Sang Yup Lee, and Jaeheung Park. "Whole-body Control of Non-holonomic Mobile Manipulator Based on Hierarchical Quadratic Programming and Continuous Task Transition." In 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2019. http://dx.doi.org/10.1109/icarm.2019.8834269.

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