Relevant bibliographies by topics / Stochastic robust control

Contents

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

Academic literature on the topic 'Stochastic robust control'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Stochastic robust control"

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Pun, Chi Seng. "Robust time-inconsistent stochastic control problems." Automatica 94 (August 2018): 249–57. http://dx.doi.org/10.1016/j.automatica.2018.04.038.

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Benavoli, A., and L. Chisci. "Robust stochastic control based on imprecise probabilities*." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4606–13. http://dx.doi.org/10.3182/20110828-6-it-1002.02081.

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Hassan, Ali, Robert Mieth, Deepjyoti Deka, and Yury Dvorkin. "Stochastic and Distributionally Robust Load Ensemble Control." IEEE Transactions on Power Systems 35, no. 6 (November 2020): 4678–88. http://dx.doi.org/10.1109/tpwrs.2020.2992268.

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Glasserman, Paul, and Xingbo Xu. "Robust Portfolio Control with Stochastic Factor Dynamics." Operations Research 61, no. 4 (August 2013): 874–93. http://dx.doi.org/10.1287/opre.2013.1180.

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Martínez-Frutos, J., R. Ortigosa, P. Pedregal, and F. Periago. "Robust optimal control of stochastic hyperelastic materials." Applied Mathematical Modelling 88 (December 2020): 888–904. http://dx.doi.org/10.1016/j.apm.2020.07.012.

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Steinbach, Marc C. "Robust Process Control by Dynamic Stochastic Programming." PAMM 4, no. 1 (December 2004): 11–14. http://dx.doi.org/10.1002/pamm.200410003.

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Zhang, Tianliang, Yu-Hong Wang, Xiushan Jiang, and Weihai Zhang. "Robust Stability, Stabilization, andH∞Control of a Class of Nonlinear Discrete Time Stochastic Systems." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5185784.

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This paper studies robust stability, stabilization, andH∞control for a class of nonlinear discrete time stochastic systems. Firstly, the easily testing criteria for stochastic stability and stochastic stabilizability are obtained via linear matrix inequalities (LMIs). Then a robustH∞state feedback controller is designed such that the concerned system not only is internally stochastically stabilizable but also satisfies robustH∞performance. Moreover, the previous results of the nonlinearly perturbed discrete stochastic system are generalized to the system with state, control, and external disturbance dependent noise simultaneously. Two numerical examples are given to illustrate the effectiveness of the proposed results.
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Chen, Guici, and Yi Shen. "Robust ReliableH∞Control for Nonlinear Stochastic Markovian Jump Systems." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/431576.

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The robust reliableH∞control problem for a class of nonlinear stochastic Markovian jump systems (NSMJSs) is investigated. The system under consideration includes Itô-type stochastic disturbance, Markovian jumps, as well as sector-bounded nonlinearities and norm-bounded stochastic nonlinearities. Our aim is to design a controller such that, for possible actuator failures, the closed-loop stochastic Markovian jump system is exponential mean-square stable with convergence rateαand disturbance attenuationγ. Based on the Lyapunov stability theory and Itô differential rule, together with LMIs techniques, a sufficient condition for stochastic systems is first established in Lemma 3. Then, using the lemma, the sufficient conditions of the solvability of the robust reliableH∞controller for linear SMJSs and NSMJSs are given. Finally, a numerical example is exploited to show the usefulness of the derived results.
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Wang, Cheng, and Cong Jun Rao. "Study on Stability and Robust Control of Stochastic Network Control System." Applied Mechanics and Materials 602-605 (August 2014): 1023–26. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1023.

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Network control system is a feedback control system of realizing the exchange of information control system in different regional components by using the digital communication. Focusing on the stability and robust control of stochastic network control system, this paper introduces the research history and the newest research trends, and presents many widespread theoretical and applications problems. Moreover, assumptions, main ideas, and conclusions of literature related to stochastic network control system are reviewed and commented.
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Schacher, Michael. "Optimal control of robots under stochastic uncertainty: robust feedback control." PAMM 7, no. 1 (December 2007): 1061801–2. http://dx.doi.org/10.1002/pamm.200700037.

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Dissertations / Theses on the topic "Stochastic robust control"

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Cheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.

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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.

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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.

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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of ‘sample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Evans, Martin A. "Multiplicative robust and stochastic MPC with application to wind turbine control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.

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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.
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Hua, H. "Optimal and robust control for a class of nonlinear stochastic systems." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3001023/.

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This thesis focuses on theoretical research of optimal and robust control theory for a class of nonlinear stochastic systems. The nonlinearities that appear in the diffusion terms are of a square-root type. Under such systems the following problems are investigated: optimal stochastic control in both finite and infinite horizon; robust stabilization and robust H∞ control; H₂/H∞ control in both finite and infinite horizon; and risk-sensitive control. The importance of this work is that explicit optimal linear controls are obtained, which is a very rare case in the nonlinear system. This is regarded as an advantage because with explicit solutions, our work becomes easier to be applied into the real problems. Apart from the mathematical results obtained, we have also introduced some applications to finance.
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Kim, Dongwook Sawan Edwin M. "Application of stochastic control and robust stability of singularly perturbed unified systems." Diss., Click here for available full-text of this thesis, 2006. http://library.wichita.edu/digitallibrary/etd/2006/t026.pdf.

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Thesis (M.S.)--Wichita State University, Dept. of Electrical Engineering.
"August 2006." Title from PDF title page (viewed on October 2, 2006). Thesis adviser: Edwin M. Sawan. Includes bibliographic references (leaves 50-53).
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Song, Miao Ph D. Massachusetts Institute of Technology. "Applications of stochastic inventory control in market-making and robust supply chains." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62049.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 169-172).
This dissertation extends the classical inventory control model to address stochastic inventory control problems raised in market-making and robust supply chains. In the financial market, market-makers assume the role of a counterpart so that investors can trade any fixed amounts of assets at quoted bid or ask prices at any time. Market-makers benefit from the spread between the bid and ask prices. but they have to carry inventories of assets which expose them to potential losses when the market price moves in an undesirable direction. One approach to reduce the risk associated with price uncertainty is to actively trade with other Market-Makers at the price of losing potential spread gain. We propose a dynamic programming model to determine the optimal active trading quantity., which maximizes the Market-Maker's expected utility. For a single-asset model. We show that a threshold inventory control policy is optimal with respect to both an exponential utility criterion and a mean-variance tradeoff objective. Special properties such as symmetry and monotonicity of the threshold levels are also investigated. For a multiple-asset model. the mean-variance analysis suggests that there exists a connected no-trade region such that the Market-Maker does not need to actively trade with other market-makers if the inventory falls in the no-trade region. Outside the no-trade region. the optimal way to adjust inventory levels can be obtained from the boundaries of the no-trade region. These properties of the optimal policy lead to practically efficient algorithms to solve the problem. The dissertation also considers the stochastic inventory control model in robust supply chain systems. Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control policy using the estimated distributions. which often leads to fragile solutions in case the preselected family of distributions was inadequate. In this work. we propose a minimax robust model that integrates data fitting and inventory optimization for the single item multi-period periodic review stochastic lot-sizing problem. Unlike the classical stochastic inventory models, where demand distribution is known, we assume that histograms are part of the input. The robust model generalizes Bayesian model, and it can be interpreted as minimizing history dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular., we analyze the robust models based on the chi-square goodness-of-fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under general conditions.
by Miao Song.
Ph.D.
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Yang, Lin. "Linear robust H-infinity stochastic control theory on the insurance premium-reserve processes." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2037227/.

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This thesis deals with the stability analysis of linear discrete-time premium-reserve (P-R) systems in a stochastic framework. Such systems are characterised by a mixture of the premium pricing process and the medium- and long- term stability in the accumulated reserve (surplus) policy, and they play a key role in the modern actuarial literature. Although the mathematical and practical analysis of P-R systems is well studied and motivated, their stability properties have not been studied thoughtfully and they are restricted in a deterministic framework. In Engineering, during the last three decades, many useful techniques are developed in linear robust control theory. This thesis is the first attempt to use some useful tools from linear robust control theory in order to analyze the stability of these classical insurance systems. Analytically, in this thesis, P-R systems are first formulated with structural properties such that time-varying delays, random disturbance and parameter uncertainties. Then as an extension of the previous literature, the results of stabilization and the robust H-infinity control of P-R systems are modelled in stochastic framework. Meanwhile, the risky investment impact on the P-R system stability condition is shown. In this approach, the potential effects from changes in insurer's investment strategy is discussed. Next we develop regime switching P-R systems to describe the abrupt structural changes in the economic fundamentals as well as the periodic switches in the parameters. The results for the regime switching P-R system are illustrated by means of two different approaches: markovian and arbitrary regime switching systems. Finally, we show how robust guaranteed cost control could be implemented to solve an optimal insurance problem. In each chapter, Linear Matrix Inequality (LMI) sufficient conditions are derived to solve the proposed sub-problems and numerical examples are given to illustrate the applicability of the theoretical findings.
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Paul, Anand Abraham. "Stochastic models in the analysis of project subcontracting and robustness to variability in project networks." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992885.

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Azad, Saeed. "Combined Design and Control Optimization of Stochastic Dynamic Systems." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1602153122063302.

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Books on the topic "Stochastic robust control"

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Weinmann, A. Uncertain models and robust control. Wien: Springer-Verlag, 1991.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. Mathematical Methods in Robust Control of Linear Stochastic Systems. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3.

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Lihua, Xie, Popa Dan 1969-, and Lewis Frank L, eds. Optimal and robust estimation: With an introduction to stochastic control theory. 2nd ed. Boca Raton: CRC Press, 2008.

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Toader, Morozan, Stoica Adrian, and SpringerLink (Online service), eds. Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems. New York, NY: Springer Science+Business Media, LLC, 2010.

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Lewis, Frank L. Optimal and robust estimation: With an introduction to stochastic control theory. 2nd ed. Boca Raton: CRC Press/Taylor & Francis, 2007.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0630-4.

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Fei hang dao dan zhi dao kong zhi xi tong sui ji lu bang fen xi yu she ji: Stochastic robustness analysis and design for guidance and control system of winged missile. Beijing Shi: Guo fang gong ye chu ban she, 2010.

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Robuste Frontierfunktionen, methodologische Anmerkungen und Ausbildungsadäquanzmessung. Frankfurt am Main: P. Lang, 2001.

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Rigatos, Gerasimos G. Intelligent industrial systems: Modeling, automation and adaptive behavior. Hershey, PA: Information Science Reference, 2010.

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Weinmann, Alexander. Uncertain Models and Robust Control. Springer, 2012.

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Book chapters on the topic "Stochastic robust control"

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Inaba, Hiroshi, and Naohisa Otsuka. "Stochastic Disturbance Decoupling." In Robust Control of Linear Systems and Nonlinear Control, 397–406. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_38.

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Petersen, Ian R., Valery A. Ugrinovskii, and Andrey V. Savkin. "Robust control of stochastic uncertain systems." In Communications and Control Engineering, 245–345. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0447-6_8.

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Boukas, El-Kébir, and Zi-Kuan Liu. "Robust ‘H∞Control, Filtering, and Guaranteed Cost Control." In Deterministic and Stochastic Time-Delay Systems, 131–73. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0077-2_6.

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Øksendal, Bernt, and Agnès Sulem. "Robust Stochastic Control and Equivalent Martingale Measures." In Stochastic Analysis with Financial Applications, 179–89. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0348-0097-6_12.

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Hinrichsen, D., and A. J. Pritchard. "Robust stability of linear stochastic systems." In Open Problems in Mathematical Systems and Control Theory, 125–29. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0807-8_26.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. "Stochastic H 2 Optimal Control." In Mathematical Methods in Robust Control of Linear Stochastic Systems, 287–326. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3_7.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. "Robust Stabilization of Linear Stochastic Systems." In Mathematical Methods in Robust Control of Linear Stochastic Systems, 381–436. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3_9.

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Yao, Xiuming, Ligang Wu, and Wei Xing Zheng. "Robust Filtering of Markovian Jump Stochastic Systems." In Studies in Systems, Decision and Control, 13–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31915-5_2.

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Marti, Kurt. "Stochastic Optimization Methods in Robust Adaptive Control of Robots." In Online Optimization of Large Scale Systems, 545–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8_28.

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de Souza, Carlos E., and Marcelo D. Fragoso. "Results on Generalised Riccati Equations Arising in Stochastic Control." In Robust Control of Linear Systems and Nonlinear Control, 95–102. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_6.

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Conference papers on the topic "Stochastic robust control"

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HomChaudhuri, Baisravan. "Distributionally Robust Stochastic Model Predictive Control for Collision Avoidance." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9160.

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Abstract This paper focuses on distributionally robust controller design for avoiding dynamic and stochastic obstacles whose exact probability distribution is unknown. The true probability distribution of the disturbance associated with an obstacle, although unknown, is considered to belong to an ambiguity set that includes all the probability distributions that share the same first two moment. The controller thus focuses on ensuring the satisfaction of the probabilistic collision avoidance constraints for all probability distributions in the ambiguity set, hence making the solution robust to the true probability distribution of the stochastic obstacles. Techniques from robust optimization methods are used to model the distributionally robust probabilistic or chance constraints as a semi-definite programming (SDP) problem with linear matrix inequality (LMI) constraints that can be solved in a computationally tractable fashion. Simulation results for a robot obstacle avoidance problem shows the efficacy of our method.
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Sun, Haiyi, Qingling Zhang, and Ning Li. "Robust passive control for stochastic networked control systems." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5194987.

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George, Jemin, and Khanh Pham. "Robust Statistical Controller for Stochastic Systems." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5630.

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Rueckert, Elmar, Max Mindt, Jan Peters, and Gerhard Neumann. "Robust policy updates for stochastic optimal control." In 2014 IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids 2014). IEEE, 2014. http://dx.doi.org/10.1109/humanoids.2014.7041389.

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Taylor, C. J. "Robust PIP control of multivariable stochastic systems." In IEE Colloquium on Robust Control: Theory, Software and Applications. IEE, 1997. http://dx.doi.org/10.1049/ic:19971286.

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George, Jemin, and Richard Linares. "Robust estimator for uncertain stochastic systems." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5716963.

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Xu, Yunjun. "Nonlinear robust stochastic control for unmanned aerial vehicles." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5159978.

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Bernstein, Dennis S., and Scott W. Greeley. "Robust Output-Feedback Stabilization: Deterministic and Stochastic Perspectives." In 1986 American Control Conference. IEEE, 1986. http://dx.doi.org/10.23919/acc.1986.4789221.

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Mukaidani, Hiroaki, Masaru Unno, Hua Xu, and Vasile Dragan. "Soft-constrained robust equilibria in stochastic differential games." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580557.

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Aihara, S. I., and A. Bagchi. "Robust nonlinear filtering of stochastic volatility in finance." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076131.

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