Relevant bibliographies by topics / Robust Control

Academic literature on the topic 'Robust Control'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Robust Control"

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DERAWI, Dafizal, Nurul Dayana SALIM, Hairi ZAMZURI, Yangi YI, Kenzo NONAMI, and Daisuke IWAKURA. "A215 Image-based Robust Hovering Control of Multirotor Aeril Robot." Proceedings of the Symposium on the Motion and Vibration Control 2015.14 (2015): 267–72. http://dx.doi.org/10.1299/jsmemovic.2015.14.267.

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EISAKA, Toshio. "Robust control." Journal of the Japan Society for Precision Engineering 56, no. 6 (1990): 1014–19. http://dx.doi.org/10.2493/jjspe.56.1014.

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YIN, Yingjie, and Yoshikazu HAYAKAWA. "Robust Control and Adaptive Robust Control for Robot Manipulators." Transactions of the Japan Society of Mechanical Engineers Series C 67, no. 657 (2001): 1507–14. http://dx.doi.org/10.1299/kikaic.67.1507.

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Sabzevary, A. Sanai, Yasuo Tamura, and Shinichi Iwamoto. "Robust Generator Control with Robust Observer." IEEJ Transactions on Power and Energy 116, no. 3 (1996): 275–84. http://dx.doi.org/10.1541/ieejpes1990.116.3_275.

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Postlethwaite, Ian, Matthew C. Turner, and Guido Herrmann. "ROBUST CONTROL APPLICATIONS." IFAC Proceedings Volumes 39, no. 9 (2006): 713–25. http://dx.doi.org/10.3182/20060705-3-fr-2907.00122.

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Toumodge, S. "Robust Control [Bookshelf]." IEEE Control Systems 16, no. 4 (August 1996): 93. http://dx.doi.org/10.1109/mcs.1996.526917.

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Rocke, David M. "Robust Control Charts." Technometrics 31, no. 2 (May 1989): 173–84. http://dx.doi.org/10.1080/00401706.1989.10488511.

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Böhm, Josef. "Robust adaptive control." Automatica 37, no. 5 (May 2001): 793–95. http://dx.doi.org/10.1016/s0005-1098(01)00021-8.

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Miyasato, Yoshihiko. "Robust adaptive control." Automatica 38, no. 9 (September 2002): 1628–30. http://dx.doi.org/10.1016/s0005-1098(02)00059-6.

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Ackermann, Juergen, Bob Barmish, John Doyle, Georg Gruebel, Ian Petersen, and M. Vidyasagar. "14.6 — Robust Control." IFAC Proceedings Volumes 20, no. 5 (July 1987): 117. http://dx.doi.org/10.1016/s1474-6670(17)55548-2.

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Dissertations / Theses on the topic "Robust Control"

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Nakashima, Paulo Hiroaqui Ruiz. "Controle H2, H∞ e H2/H∞ aplicados a um robô manipulador subatuado." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-08062017-122423/.

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Este trabalho apresenta os resultados da aplicação de três técnicas de controle utilizadas no projeto e implementação do controle de um manipulador subatuado planar de três juntas em série e de elos rígidos, projetado e construído pela Universidade Carnegie Mellon, EUA. Devido ao alto grau de não-linearidade deste sistema, seria muito difícil implementar um controlador H2, H∞ ou H2/H∞ que atuasse sozinho. Assim, propõe-se a utilização de um método de controle combinado: torque computado/H2, H∞ ou H2/H∞. No controle combinado, a porção correspondente ao torque computado lineariza e pré-compensa a dinâmica do modelo da planta nominal, enquanto a porção correspondente ao controle H2, H∞ ou H2/H∞ realiza uma pós-compensação dos erros residuais, que não foram completamente eliminados pelo método torque computado. Testes de acompanhamento de trajetória e testes de robustez são realizados aqui para comprovar a eficiência destes controladores, com resultados de implementação bastante satisfatórios.
This work presents the application results of three control techniques used for the control design and implementation of a serial planar underactuated manipulator with three joints and rigid links, designed and built by the Carnegie Mellon University, USA. Due to the high non-linearity degree of this system, it would be very difficult to implement an H2, H∞ or H2/ H∞ control which would actuate on the system by itself. Therefore, it is proposed a combined control method: computed torque/ H2, H∞ or H2/H∞. In the combined control, the portion corresponding to the computed torque linearizes and pre-compensates the dynamics of the nominal model, while the portion corresponding to the H2, H∞ or H2/H∞ control realizes a pos-compensation of the residual errors, not completely removed by the computed torque method. Trajetory tracking and robustness tests are performed here to demonstrate the efficiency of these controllers, with very satisfatory implementation results.
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Bekit, Biniam Weldai. "Robust nonlinear control of robot manipulators." Thesis, King's College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321945.

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Löhning, Matthias [Verfasser]. "Robust Control of Elastic Robots / Matthias Löhning." München : Verlag Dr. Hut, 2011. http://d-nb.info/1015606512/34.

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Cetinyurek, Aysun. "Robust Control Charts." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12607987/index.pdf.

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ABSTRACT ROBUST CONTROL CHARTS Ç
etinyü
rek, Aysun M. Sc., Department of Statistics Supervisor: Dr. BariS Sü
rü
cü
Co-Supervisor: Assoc. Prof. Dr. Birdal Senoglu December 2006, 82 pages Control charts are one of the most commonly used tools in statistical process control. A prominent feature of the statistical process control is the Shewhart control chart that depends on the assumption of normality. However, violations of underlying normality assumption are common in practice. For this reason, control charts for symmetric distributions for both long- and short-tailed distributions are constructed by using least squares estimators and the robust estimators -modified maximum likelihood, trim, MAD and wave. In order to evaluate the performance of the charts under the assumed distribution and investigate robustness properties, the probability of plotting outside the control limits is calculated via Monte Carlo simulation technique.
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Fu, Ye. "Robust adaptive control." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/30574.

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This thesis describes discrete robust adaptive control methods based on using slow sampling and slow adaptation. For the stability analysis, we consider that the plant model order is not exactly known and assume that the estimation model order is lower than the plant model order. A stability condition is derived with a given upper limit for the adaptation gain which is related to a strictly positive real operator. Discussion of the relation between sampling and stability condition is then given. For the robust adaptive control design, we study slow adaptation and predictive control. For the slow adaptation, the main idea is to use only good estimates and use a compensation filter. Some frequency domain information on the plant is necessary for this method. For predictive control, we discuss the relationship between the extended control horizon and the critical sampling. For a simple case, it is shown that the larger extended control horizon brings more robust adaptive control. The purpose of this thesis is to provide robust discrete adaptive controller design guidelines, especially in such cases as using slow sampling frequency, slow adaptation rate. It is true that in practice, for various discrete adaptive control algorithms, slow sampling and slow adaptation rate will bring more robustness. The use of slow sampling and slow adaptation rate is simple and economic, thus a careful choice of sampling rate and adaptation rate is highly recommended. This thesis provides such guidelines for choosing proper sampling rate and adaptation rate for robust discrete adaptive control.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Shen, Dennis Ph D. Massachusetts Institute of Technology. "Robust synthetic control." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115743.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-65).
In this thesis, we present a robust generalization of the synthetic control method. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, functions without extraneous covariates (vital to existing methods), and overcomes missing data (never been addressed in prior works). To our knowledge, we provide the first theoretical finite sample analysis for a broader class of models than previously considered in literature. Additionally, we relate the inference quality of our estimator to the amount of training data available and show our estimator to be asymptotically consistent. In order to move beyond point estimates, we introduce a Bayesian framework that not only provides practitioners the ability to readily develop different estimators under various loss functions, but also equips them with the tools to quantitatively measure the uncertainty of their model/estimates through posterior probabilities. Our empirical results demonstrate that our robust generalization yields a positive impact over the classical synthetic control method, underscoring the value of our key de-noising procedure.
by Dennis Shen.
S.M.
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Liu, Chengyuan. "Robust model predictive control : robust control invariant sets and efficient implementation." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/45529.

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Robust model predictive control (RMPC) is widely used in industry. However, the online computational burden of this algorithm restricts its development and application to systems with relatively slow dynamics. We investigate this problem in this thesis with the overall aim of reducing the online computational burden and improving the online efficiency. In RMPC schemes, robust control invariant (RCI) sets are vitally important in dealing with constraints and providing stability. They can be used as terminal (invariant) sets in RMPC schemes to reduce the online computational burden and ensure stability simultaneously. To this end, we present a novel algorithm for the computation of full-complexity polytopic RCI sets, and the corresponding feedback control law, for linear discrete-time systems subject to output and initial state constraints, performance bounds, and bounded additive disturbances. Two types of uncertainty, structured norm-bounded and polytopic uncertainty, are considered. These algorithms are then extended to deal with systems subject to asymmetric initial state and output constraints. Furthermore, the concept of RCI sets can be extended to invariant tubes, which are fundamental elements in tube based RMPC scheme. The online computational burden of tube based RMPC schemes is largely reduced to the same level as model predictive control for nominal systems. However, it is important that the constraint tightening that is needed is not excessive, otherwise the performance of the MPC design may deteriorate, and there may even not exist a feasible control law. Here, the algorithms we proposed for RCI set approximations are extended and applied to the problem of reducing the constraint tightening in tube based RMPC schemes. In order to ameliorate the computational complexity of the online RMPC algorithms, we propose an online-offline RMPC method, where a causal state feedback structure on the controller is considered. In order to improve the efficiency of the online computation, we calculate the state feedback gain offline using a semi-definite program (SDP). Then we propose a novel method to compute the control perturbation component online. The online optimization problem is derived using Farkas' Theorem, and then approximated by a quadratic program (QP) to reduce the online computational burden. A further approximation is made to derive a simplified online optimization problem, which results in a large reduction in the number of variables. Numerical examples are provided that demonstrate the advantages of all our proposed algorithms over current schemes.
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Nguyen, Quan T. "Robust and Adaptive Dynamic Walking of Bipedal Robots." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1102.

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Legged locomotion has several interesting challenges that need to be addressed, such as the ability of dynamically walk over rough terrain like stairs or stepping stones, as well as the ability to adapt to unexpected changes in the environment and the dynamic model of the robot. This thesis is driven towards solving these challenges and makes contributions on theoretical and experimental aspects to address: dynamic walking, model uncertainty, and rough terrain. On the theoretical front, we introduce and develop a unified robust and adaptive control framework that enables the ability to enforce stability and safety-critical constraints arising from robotic motion tasks under a high level of model uncertainty. We also present a novel method of walking gait optimization and gait library to address the challenge of dynamic robotic walking over stochastically generated stepping stones with significant variations in step length and step height, and where the robot has knowledge about the location of the next discrete foothold only one step ahead. On the experimental front, our proposed methods are successfully validated on ATRIAS, an underactuated, human-scale bipedal robot. In particular, experimental demonstrations illustrate our controller being able to dynamically walk at 0.6 m/s over terrain with step length variation of 23 to 78 cm, as well as simultaneous variation in step length and step height of 35 to 60cm and -22 to 22cm respectively. In addition to that, we also successfully implemented our proposed adaptive controller on the robot, which enables the ability to carry an unknown load up to 68 lb (31 kg) while maintaining very small tracking errors of about 0.01 deg (0.0017 rad) at all joints. To be more specific, we firstly develop robust control Lyapunov function based quadratic program (CLFQP) controller and L1 adaptive control to handle model uncertainty for bipedal robots. An application is dynamic walking while carrying an unknown load. The robust CLF-QP controller can guarantee robustness via a quadratic program that can be extended further to achieve robust safety-critical control. The L1 adaptive control can estimate and adapt to the presence of model uncertainty in the system dynamics. We then present a novel methodology to achieve dynamic walking for underactuated and hybrid dynamcal bipedal robots subject to safety-critical constraints. The proposed controller is based on the combination of control Barrier functions (CBFs) and control Lyapunov functions (CLFs) implemented as a state-based online quadratic program to achieve stability under input and state constraints. The main contribution of this work is the control design to enable stable dynamical bipedal walking subject to strict safety constraints that arise due to walking over a terrain with randomly generated discrete footholds. We next introduce Exponential Control Barrier Functions (ECBFs) as means to enforce high relativedegree safety constraints for nonlinear systems. We also develop a systematic design method that enables creating the Exponential CBFs for nonlinear systems making use of tools from linear control theory. Our method creates a smooth boundary for the safety set via an exponential function, therefore is called Exponential CBFs. Similar to exponential stability and linear control, the exponential boundary of our proposed method helps to have smoother control inputs and guarantee the robustness under model uncertainty. The proposed control design is numerically validated on a relative degree 4 nonlinear system (the two-link pendulum with elastic actuators and experimentally validated on a relative degree 6 linear system (the serial cart-spring system). Thanks to these advantages of Exponential CBFs, we then can apply the method to the problem of 3D dynamic walking with varied step length and step width as well as dynamic walking on time-varying stepping stones. For the work of using CBF for stepping stones, we use only one nominal walking gait. Therefore the range of step length variation is limited ([25 : 60](cm)). In order to improve the performance, we incorporate CBF with gait library and increase the step length range significantly ([10 : 100](cm)). While handling physical constraints and step transition via CBFs appears to work well, these constraints often become active at step switching. In order to resolve this issue, we introduce the approach of 2-step periodic walking. This method not only gives better step transitions but also offers a solution for the problem of changing both step length and step height. Experimental validation on the real robot was also successful for the problem of dynamic walking on stepping stones with step lengths varied within [23 : 78](cm) and average walking speed of 0:6(m=s). In order to address the problems of robust control and safety-critical control in a unified control framework, we present a novel method of optimal robust control through a quadratic program that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems under significant model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on different applications ranging from a single-link pendulum to dynamic walking of bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to significant model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations. To solve this problem, we also present another solution of adaptive CBF-CLF controller, that enables the ability to adapt to the effect of model uncertainty to maintain both stability and safety. In comparison with the robust CBF-CLF controller, this method not only can handle a higher level of model uncertainty but is also less aggressive if there is no model uncertainty presented in the system.
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Yu, Hongnian. "Modelling and robust adaptive control of robot manipulators." Thesis, King's College London (University of London), 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720361.

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Yoon, Tae-Woong. "Robust adaptive predictive control." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359527.

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Books on the topic "Robust Control"

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Siqueira, Adriano A. G., Marco H. Terra, and Marcel Bergerman. Robust Control of Robots. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-898-0.

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Ackermann, Jürgen, Dieter Kaesbauer, Wolfgang Sienel, Reinhold Steinhauser, and Andrew Bartlett. Robust Control. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3365-0.

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Ackermann, Jürgen. Robust Control. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0207-6.

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Davisson, L. D., A. G. J. MacFarlane, H. Kwakernaak, J. L. Massey, Ya Z. Tsypkin, A. J. Viterbi, and Shigeyuki Hosoe, eds. Robust Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0114641.

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Liu, Kang-Zhi, and Yu Yao. Robust Control. Singapore: John Wiley & Sons Singapore Pte. Ltd, 2016. http://dx.doi.org/10.1002/9781119113072.

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Peter, Dorato, ed. Robust control. New York: Institute of Electrical and Electronics Engineers, 1987.

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Huibert, Kwakernaak, and Ackermann Jürgen, eds. Robust control. Oxford: Pergamon, 1993.

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1961-, Sun Jing, ed. Robust adaptive control. Upper Saddle River, NJ: PTR Prentice-Hall, 1996.

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Mackenroth, Uwe. Robust Control Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09775-5.

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Magni, Jean-François, Samir Bennani, and Jan Terlouw, eds. Robust Flight Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0113842.

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Book chapters on the topic "Robust Control"

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Westphal, L. C. "Robust control." In Sourcebook of Control Systems Engineering, 797–826. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_33.

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Jaulin, Luc, Michel Kieffer, Olivier Didrit, and Éric Walter. "Robust Control." In Applied Interval Analysis, 187–223. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0249-6_7.

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Featherstone, Andrew P., Jeremy G. VanAntwerp, and Richard D. Braatz. "Robust Control." In Advances in Industrial Control, 87–128. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0413-1_6.

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Westphal, Louis C. "Robust control." In Handbook of Control Systems Engineering, 895–945. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_36.

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Skogestad, Sigurd. "Robust Control." In Practical Distillation Control, 291–309. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-0277-4_14.

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Rogers, Graham. "Robust Control." In Power System Oscillations, 199–251. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4561-3_9.

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Dixon, Warren E. "Robust Control." In Encyclopedia of Robotics, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-41610-1_96-1.

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Boukas, El-Kébir, and Fouad M. AL-Sunni. "Robust Control." In Mechatronic Systems, 383–429. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22324-2_9.

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Rao, Ming, Qijun Xia, and Yiqun Ying. "Robust Control." In Modeling and Advanced Control for Process Industries, 53–70. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2094-0_3.

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Williams, Noah. "Robust Control." In The New Palgrave Dictionary of Economics, 11775–81. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2761.

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Conference papers on the topic "Robust Control"

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Khan, Bilal, and John Anthony Rossiter. "Robust MPC algorithms using alternative parameterisations." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334748.

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Najafi, M., F. Sheikholeslam, and S. Hosseinnia. "Robust predictor for uncertain dead time systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334783.

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Shaik, Mukarram K., and James F. Whidborne. "Robust sliding mode control of a quadrotor." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737529.

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Zohdy, M. A., and A. A. Zaher. "Robust control of biped robots." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.879366.

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Larbah, Eshag, and Ron J. Patton. "Robust decentralized control design using integral sliding mode control." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334610.

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Kablar, Natasa A., and Vlada Kvrgic. "Inverse optimal robust control of singularly impulsive dynamical systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334667.

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Berk Gezer, R., and Ali Turker Kutay. "Robust model following control design for missile roll autopilot." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915107.

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Dughman, S. S., and J. A. Rossiter. "Efficient robust feed forward model predictive control with tracking." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737593.

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Lu, Xiaonan, and Mark Cannon. "Adaptive Robust MPC: Combining Robustness with Online Performance Enhancement." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516719.

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Tang, Yimeng, and Ron J. Patton. "Phase modulation of robust variable structure control for nonlinear aircraft." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334625.

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Reports on the topic "Robust Control"

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Enns, Dale. Robust Flight Control. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada411755.

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Morton, Blaise. Advances in Robust Control. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada291126.

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Valavani, Lena. Robust and Adaptive Control. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada224810.

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Safonov, Michael G. Robust Control Feedback and Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada399708.

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Roseta Palma, Catarina, and Anastasios Xepapadeas. Robust Control in Water Management. DINÂMIA'CET-IUL, 2002. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2002.24.

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Surka, Derek M. ObjectAgent for Robust Autonomous Control. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada451691.

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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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Ikeda, Yutaka, James Ramsey, Eugene Lavretsky, and Patrick McCormick. Robust Adaptive Control of UCAVs. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada427938.

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Doyle, John C. Robust Control Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada337888.

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McEneaney, William M. Nonlinear Robust Control and Estimation. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada383810.

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