Relevant bibliographies by topics / The task of optimal control

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The task of optimal control'

Author: Grafiati
Published: 4 May 2022
Last updated: 5 May 2022

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Journal articles on the topic "The task of optimal control"

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KOITABASHI, Tatsuo, and Hikaru INOOKA. "Control behavior of human operator in an optimal control task." Japanese journal of ergonomics 23, no. 5 (1987): 317–24. http://dx.doi.org/10.5100/jje.23.317.

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Yao, Yuhua, and Jitao Sun. "Optimal control of multi-task Boolean control networks via temporal logic." Systems & Control Letters 156 (October 2021): 105007. http://dx.doi.org/10.1016/j.sysconle.2021.105007.

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Mistry, Michael, Evangelos Theodorou, Stefan Schaal, and Mitsuo Kawato. "Optimal control of reaching includes kinematic constraints." Journal of Neurophysiology 110, no. 1 (2013): 1–11. http://dx.doi.org/10.1152/jn.00794.2011.

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We investigate adaptation under a reaching task with an acceleration-based force field perturbation designed to alter the nominal straight hand trajectory in a potentially benign manner: pushing the hand off course in one direction before subsequently restoring towards the target. In this particular task, an explicit strategy to reduce motor effort requires a distinct deviation from the nominal rectilinear hand trajectory. Rather, our results display a clear directional preference during learning, as subjects adapted perturbed curved trajectories towards their initial baselines. We model this behavior using the framework of stochastic optimal control theory and an objective function that trades off the discordant requirements of 1) target accuracy, 2) motor effort, and 3) kinematic invariance. Our work addresses the underlying objective of a reaching movement, and we suggest that robustness, particularly against internal model uncertainly, is as essential to the reaching task as terminal accuracy and energy efficiency.
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Gong, Yanmin, Lingbo Wei, Yuanxiong Guo, Chi Zhang, and Yuguang Fang. "Optimal Task Recommendation for Mobile Crowdsourcing With Privacy Control." IEEE Internet of Things Journal 3, no. 5 (2016): 745–56. http://dx.doi.org/10.1109/jiot.2015.2512282.

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Pepelyaeva, T. V., L. B. Vovk, and I. Yu Demchenko. "Optimal Strategies for the Multi-Task Inventory Control Model." Cybernetics and Systems Analysis 52, no. 1 (2016): 107–12. http://dx.doi.org/10.1007/s10559-016-9805-6.

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Lee, Jaemin, Efstathios Bakolas, and Luis Sentis. "Hierarchical Task-Space Optimal Covariance Control With Chance Constraints." IEEE Control Systems Letters 6 (2022): 2359–64. http://dx.doi.org/10.1109/lcsys.2022.3153094.

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Galicki, M. "Kinematically Optimal Robust Control of Redundant Manipulators." International Journal of Applied Mechanics and Engineering 22, no. 4 (2017): 839–65. http://dx.doi.org/10.1515/ijame-2017-0055.

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Abstract This work deals with the problem of the robust optimal task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the endeffector. Furthermore, the movement is to be accomplished in such a way as to minimize both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of chattering-free robust kinematically optimal controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.
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Lewis, Debra. "Modeling student engagement using optimal control theory." Journal of Geometric Mechanics 14, no. 1 (2022): 131. http://dx.doi.org/10.3934/jgm.2021032.

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<p style='text-indent:20px;'>Student engagement in learning a prescribed body of knowledge can be modeled using optimal control theory, with a scalar state variable representing mastery, or self-perceived mastery, of the material and control representing the instantaneous cognitive effort devoted to the learning task. The relevant costs include emotional and external penalties for incomplete mastery, reduced availability of cognitive resources for other activities, and psychological stresses related to engagement with the learning task. Application of Pontryagin's maximum principle to some simple models of engagement yields solutions of the synthesis problem mimicking familiar behaviors including avoidance, procrastination, and increasing commitment in response to increasing mastery.</p>
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Gu, Edward Y. L., and Naim A. Kheir. "Optimal task planning and control of multi-robot coordinated workcells." IFAC Proceedings Volumes 32, no. 2 (1999): 671–76. http://dx.doi.org/10.1016/s1474-6670(17)56114-5.

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Diedrichsen, Jörn. "Optimal Task-Dependent Changes of Bimanual Feedback Control and Adaptation." Current Biology 17, no. 19 (2007): 1675–79. http://dx.doi.org/10.1016/j.cub.2007.08.051.

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Dissertations / Theses on the topic "The task of optimal control"

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Audren, Hervé. "On multi-contact dynamic motion using reduced models." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS012/document.

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Pour les robots marcheurs, c'est à dire bipèdes, quadrupèdes, hexapodes, etc... la notion de stabilité est primordiale. En effet, ces robots possèdent une base flottante sous-actuée : il leur faut prendre appui sur l'environnement pour se mouvoir. Toutefois, cette caractéristique les rend vulnérables: ils peuvent tomber. Il est donc indispensable de pouvoir différencier un mouvement stable d'un mouvement non-stable. Dans cette thèse, la stabilité est considérée du point de vue d'un modèle réduit au Centre de Masse (ou Centre de Gravité). Nous montrons dans un premier temps comment calculer la zone de stabilité de ce modèle dans le cas statique. Bien que cette région soit un objet purement géométrique, nous montrons qu'elle dépend des forces de contact admissibles. Ensuite, nous montrons qu'introduire la notion de robustesse, c'est à dire une marge d'incertitude sur les accélérations (ou les forces de contacts) transforme la forme plane du cas statique en un volume tridimensionnel. Afin de calculer cette forme, nous présentons de nouveaux algorithmes récursifs. Nous appliquons ensuite des algorithmes provenant de l'infographie qui permettent de déformer continûment ces objets géométriques. Cette transformation nous permet d'approximer des changements dans les variables influençant ces formes. Calculer le volume de stabilité explicitement nous permet de découpler les accélérations des positions du CdM, ce qui nous permet de formuler un problème de contrôle prédictif linéaire. Nous proposons aussi une autre formulation linéaire qui, au prix de calculs plus coûteux, permet d'exploiter pleinement la dynamique du robot. Enfin, nous appliquons ces résultats dans une approche hiérarchique qui nous permet de générer des mouvements du corps complet du robot, aussi bien sur une véritable plateforme humanoïde qu'en simulation<br>In the context of legged robotics, stability (or equilibrium) is of the utmost importance. Indeed, as legged robots have a non-actuated floating base they can fall. To avoid falling, we must be able to tell apart stable from non-stable motion. This thesis approaches stability from a reduced model point-of-view: our main interest is the Center of Mass. We show how to compute stability regions for this reduced model, at first based on purely static stability. Although purely geometrical in nature, we show how they depend on the admissible contact forces. Then, we show that taking into account robustness, in the sense of acceleration (or contact forces) affordances transforms the usual two-dimensional stability region into a three dimensional one. To compute this shape, we introduce novel recursive algorithms. We show how we can apply computer graphics techniques for shape morphing in order to continuously deform the aforementioned regions. This allows us to approximate changes in the parameters of those shapes, but also to interpolate between shapes. Finally, we exploit the effective decoupling offered by the explicit computation of the stability polyhedron to formulate a linear, minimal jerk model-predictive control problem. We also propose another linear MPC problem that exploits more of the available dynamics, but at an increased computational cost. We then adopt a hierarchical approach, and use those CoM results as input to our whole-body controller. Results are demonstrated on real hardware and in simulation
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Alaya, Oussama, and Maik Fiedler. "Optimal pressure control using switching solenoid valves." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-200545.

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This paper presents the mathematical modeling and the design of an optimal pressure tracking controller for an often used setup in pneumatic applications. Two pneumatic chambers are connected with a pneumatic tube. The pressure in the second chamber is to be controlled using two switching valves connected to the first chamber and based on the pressure measurement in the first chamber. The optimal control problem is formulated and solved using the MPC framework. The designed controller shows good tracking quality, while fulfilling hard constraints, like maintaining the pressure below a given upper bound.
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Enes, Aaron R. "Shared control of hydraulic manipulators to decrease cycle time." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37251.

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This thesis presents a technique termed Blended Shared Control, whereby a human operator's commands are merged with the commands of an electronic agent in real time to control a manipulator. A four degree-of-freedom hydraulic excavator is used as an application example, and two types of models are presented: a fully dynamic model incorporating the actuator and linkage systems suitable for high-fidelity user studies, and a reduced-order velocity-constrained kinematic model amenable for real-time optimization. Intended operator tasks are estimated with a recursive algorithm; the task is optimized in real time; and a command perturbation is computed which, when summed with the operator command, results in a lower task completion time. Experimental results compare Blended Shared Control to other types of controllers including manual control and haptic feedback. Trials indicate that Blended Shared Control decreases task completion time when compared to manual operation.
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Dyson, Matthew. "Selecting optimal cognitive tasks for control of a brain computer interface." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528844.

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Francioni, Marco. "Modeling and optimal flight control of a foiling dinghy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23367/.

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This Master Thesis illustrates the physics behind the mathematical model of a foiling dinghy to be used in a model-based autopilot architecture, and the multiple frames of references needed for an exhaustive force description. Using the modeled foiling boat, we performed the non-trivial task of finding meaningful trim setpoints, which were then used throughout the simulations. We applied Optimal Control theories to achieve stability and control of a foiling dinghy with a movable crew at different trim settings and various environmental parameters, such as wind speed and sea state, both stationary and time-varying. We developed a prototype of a gain scheduler for the closed-loop to perform tack and jibes maneuvers in multiple environments, and compared the stability and parameters sensitivity of different closed feedback loop architectures, both in a straight line and maneuvering performance. The maneuvering performances were established with extensive ad-hoc simulations to properly characterize the behavior of the architecture, while the straight-line response and parameter variation sensitivity was determined through Monte Carlo simulations. At the end of this paperwork, the two best performing closed-loop architectures proposed were compared to determine which one would be the more promising for a practical application.
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Gaid, MEMB, AS Cela, and Y. Hamam. "Optimal Real-Time Scheduling of Control Tasks with State Feedback Resource Allocation." IEEE Transactions on Control Systems Technology, 2009. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001370.

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Abstract—This paper proposes a new approach for the optimal integrated control and real-time scheduling of control tasks. First, the problem of the optimal integrated control and nonpreemptive off-line scheduling of control tasks in the sense of the H2 performance criterion is addressed. It is shown that this problem may be decomposed into two sub-problems. The first sub-problem aims at finding the optimal non-preemptive off-line schedule, and may be solved using the branch and bound method. The second sub-problem uses the lifting technique to determine the optimal control gains, based on the solution of the first sub-problem. Second, an efficient on-line scheduling algorithm is proposed. This algorithm, called Reactive Pointer Placement (RPP) scheduling algorithm, uses the plant state information to dispatch the computational resources in a way that improves control performance. Control performance improvements as well as stability guarantees are formally proven. Finally, simulations as well as experimental results are presented in order to illustrate the effectiveness of the proposed approach.
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Basilewitsch, Daniel [Verfasser]. "Optimal control of quantum information tasks in open quantum systems / Daniel Basilewitsch." Kassel : Universitätsbibliothek Kassel, 2021. http://d-nb.info/1232368407/34.

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Perundurai, Rajasekaran Siddharthan. "Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/1205.

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"This thesis focuses on two key problems in reinforcement learning: How to design reward functions to obtain intended behaviors in autonomous systems using the learning-based control? Given complex mission specification, how to shape the reward function to achieve fast convergence and reduce sample complexity while learning the optimal policy? To answer these questions, the first part of this thesis investigates inverse reinforcement learning (IRL) method with a purpose of learning a reward function from expert demonstrations. However, existing algorithms often assume that the expert demonstrations are generated by the same reward function. Such an assumption may be invalid as one may need to aggregate data from multiple experts to obtain a sufficient set of demonstrations. In the first and the major part of the thesis, we develop a novel method, called Non-parametric Behavior Clustering IRL. This algorithm allows one to simultaneously cluster behaviors while learning their reward functions from demonstrations that are generated from more than one expert/behavior. Our approach is built upon the expectation-maximization formulation and non-parametric clustering in the IRL setting. We apply the algorithm to learn, from driving demonstrations, multiple driver behaviors (e.g., aggressive vs. evasive driving behaviors). In the second task, we study whether reinforcement learning can be used to generate complex behaviors specified in formal logic — Linear Temporal Logic (LTL). Such LTL tasks may specify temporally extended goals, safety, surveillance, and reactive behaviors in a dynamic environment. We introduce reward shaping under LTL constraints to improve the rate of convergence in learning the optimal and probably correct policies. Our approach exploits the relation between reward shaping and actor-critic methods for speeding up the convergence and, as a consequence, reducing training samples. We integrate compositional reasoning in formal methods with actor-critic reinforcement learning algorithms to initialize a heuristic value function for reward shaping. This initialization can direct the agent towards efficient planning subject to more complex behavior specifications in LTL. The investigation takes the initial step to integrate machine learning with formal methods and contributes to building highly autonomous and self-adaptive robots under complex missions."
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Smit, Willem Jacobus. "The optimal design of a planar Stewart platform for prescribed machining tasks." Diss., Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-01122007-134416/.

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Bountourelis, Theologos. "Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28144.

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Thesis (M. S.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Bert.
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Books on the topic "The task of optimal control"

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1934-, Tikhomirov V. M., and Fomin S. V, eds. Optimal control. Consultants Bureau, 1987.

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Optimal control. Wiley, 1986.

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Vinter, Richard. Optimal Control. Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-8086-2.

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Aschepkov, Leonid T., Dmitriy V. Dolgy, Taekyun Kim, and Ravi P. Agarwal. Optimal Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49781-5.

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Hager, William H., and Panos M. Pardalos. Optimal Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6095-8.

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L, Syrmos Vassilis, ed. Optimal control. 2nd ed. Wiley, 1995.

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Alekseev, V. M., V. M. Tikhomirov, and S. V. Fomin. Optimal Control. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-7551-1.

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Locatelli, Arturo. Optimal Control. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8328-3.

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Lewis, Frank L., Draguna L. Vrabie, and Vassilis L. Syrmos. Optimal Control. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118122631.

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Bulirsch, R., A. Miele, J. Stoer, and K. Well, eds. Optimal Control. Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-7539-4.

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Book chapters on the topic "The task of optimal control"

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Serra, Diana, Fabio Ruggiero, Aykut C. Satici, Vincenzo Lippiello, and Bruno Siciliano. "Time-Optimal Paths for a Robotic Batting Task." In Informatics in Control, Automation and Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55011-4_13.

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Lu, Dan, Qilong Han, Hongbin Zhao, and Kejia Zhang. "Optimal Task Recommendation for Spatial Crowdsourcing with Privacy Control." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6385-5_35.

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Kerimbekov, A., and O. K. Tairova. "Building Bellman-Egorov Equation for a Task of Optimal Control." In Ubiquitous Computing and the Internet of Things: Prerequisites for the Development of ICT. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13397-9_123.

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Nikulin, Vladimir. "Optimal periodic control with environmental application." In Tasks and Methods in Applied Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_402.

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Pawell, Angela. "Free Surface Waves in a Wave Tank." In Variational Calculus, Optimal Control and Applications. Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8802-8_31.

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Hoff, Bruce. "Using Optimal Control to Model Trajectory Formation and Perturbation Response in a Prehension Task." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_30.

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Palagachev, Konstantin D., and Matthias Gerdts. "Numerical Approaches Towards Bilevel Optimal Control Problems with Scheduling Tasks." In Math for the Digital Factory. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63957-4_10.

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Xiao, Ling, Gong Liang, Wu Linlizi, et al. "Multi-core Control of the Switched Reluctance Drive with High-Performance Timing Analysis and Optimal Task Deployment." In Intelligent Robotics and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22879-2_59.

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Zhang, Shaoyao, Yu Tian, Chunhui Wang, Shoupeng Huang, Yan Fu, and Shanguang Chen. "Modeling Human Control Strategies in Simulated RVD Tasks through the Time-Fuel Optimal Control Model." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07725-3_65.

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Kirschmann, Moritz A., Jörg Pierer, Alexander Steinecker, Philipp Schmid, and Arne Erdmann. "Plenoptic Inspection System for Automatic Quality Control of MEMS and Microsystems." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_16.

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AbstractOptical quality control of MEMS and microsystems is challenging as these structures are micro-scale and three dimensional. Here we lay out different optical systems that can be used for 3D optical quality control in general and for such structures in particular. We further investigate one of these technologies – plenoptic cameras and characterize them for the described task, showing advantages and disadvantages. Key advantages are a huge increase in depth of field compared to conventional microscope camera systems allowing for fast acquisition of non-flat systems and secondly the resulting total focus images and depth maps. Finally we conclude that these advantages render plenoptic cameras a valuable technology for the application of quality control.
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Conference papers on the topic "The task of optimal control"

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Wu, Z., P. Luh, S. Chang, D. Serfaty, D. Kleinman, and D. Castanon. "Optimal task allocation of two service stations with three classes of tasks." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268902.

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Hyun-Wook Jo, Jae-Ho Ahn, Jun-Sang Park, Jun-Han Oh, and Jong-Tae Lim. "Task planning for service robots with optimal supervisory control." In 2010 IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2010. http://dx.doi.org/10.1109/ramech.2010.5513172.

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Ebrahim, A. Al-Gallaf. "Robotics system optimal task control (neuro-inverse kinematics approach)." In 2006 IEEE GCC Conference. IEEE, 2006. http://dx.doi.org/10.1109/ieeegcc.2006.5686190.

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Lin, Jianping, Nikhil V. Divekar, Ge Lv, and Robert D. Gregg. "Optimal Task-Invariant Energetic Control for a Knee-Ankle Exoskeleton." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483212.

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Notomista, Gennaro, Siddharth Mayya, Seth Hutchinson, and Magnus Egerstedt. "An Optimal Task Allocation Strategy for Heterogeneous Multi-Robot Systems." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8795895.

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Di Paola, Donato, Mauro Gaggero, Antonio Petitti, and Luca Caviglione. "Optimal control of time instants for task replanning in robotic networks." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7525211.

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Polycarpou, Odysseas, Christos Anagnostopoulos, and Kostas Kolomvatsos. "Optimal Load-Aware Task Offloading in Mobile Edge Computing." In The 8th International Conference of Control, Dynamic Systems, and Robotics. Avestia Publishing, 2021. http://dx.doi.org/10.11159/cdsr21.301.

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Schüthe, Dennis, and Udo Frese. "Task Level Optimal Control of a Simulated Ball Batting Robot." In 11th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005026100450056.

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Yamaguchi, Gary T., and Ali Kakavand. "Optimal control model of arm configuration in a reaching task." In 1996 Symposium on Smart Structures and Materials, edited by Kent A. Murphy and Dryver R. Huston. SPIE, 1996. http://dx.doi.org/10.1117/12.240894.

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Karimadini, Mohammad, and Hai Lin. "Optimal task automaton decomposabilization for a class of global specifications." In 2010 8th IEEE International Conference on Control and Automation (ICCA). IEEE, 2010. http://dx.doi.org/10.1109/icca.2010.5524201.

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Reports on the topic "The task of optimal control"

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Rabitz, Herschel. Optimal Control of MoIecular Motion. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada291919.

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Shreve, S. E., and V. J. Mizel. Optimal Control with Diminishing and Zero Cost for Control. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada182805.

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Shao, Cheng, and Dimitrios Hristu-Varsakelis. Optimal Control through Biologically-Inspired Pursuit. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada439266.

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Shao, Cheng, and Dimitrios Hristu-Varsakelis. Biologically Inspired Algorithms for Optimal Control. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada439518.

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Sachs, Ekkehard W. Superlinear Convergent Algorithms in Optimal Control. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada179614.

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Desbrun, Mathieu, and Marin Kobilarov. Geometric Computational Mechanics and Optimal Control. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada564028.

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von Winckel, Gregory John. Optimal Design and Control of Qubits. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1475100.

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Ray, Asok, and Travis Ortogero. Language Measure for Robust Optimal Control. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada444858.

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Shao, Cheng, and D. Hristu-Varsakelis. Biologically-Inspired Optimal Control via Intermittent Cooperation. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada438963.

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Kessel, C. E., M. A. Firestone, and R. W. Conn. Linear optimal control of tokamak fusion devices. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6165092.

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