Bibliographies thématiques / Predictive and Adaptive Control

Littérature scientifique sur le sujet « Predictive and Adaptive Control »

Auteur : Grafiati
Publié le 14 mars 2023

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Articles de revues sur le sujet "Predictive and Adaptive Control"

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Ray, W. D. « Adaptive prediction and predictive control ». International Journal of Forecasting 12, no 4 (décembre 1996) : 566. http://dx.doi.org/10.1016/s0169-2070(96)00688-7.

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Clarke, D. W. « Adaptive predictive control ». Annual Reviews in Control 20 (janvier 1996) : 83–94. http://dx.doi.org/10.1016/s1367-5788(97)00007-2.

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Clarke, D. « Adaptive predictive control ». Annual Review in Automatic Programming 20 (1996) : 83–94. http://dx.doi.org/10.1016/s0066-4138(97)00007-4.

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Clarke, D. W. « Adaptive Predictive Control ». IFAC Proceedings Volumes 28, no 13 (juin 1995) : 43–54. http://dx.doi.org/10.1016/s1474-6670(17)45325-0.

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Esteban, Segundo, Jesus Μ. de la Cruz et Jose Μ. Girón-sierra. « Adaptive Predictive Flight Control ». IFAC Proceedings Volumes 33, no 13 (juin 2000) : 239–44. http://dx.doi.org/10.1016/s1474-6670(17)37196-3.

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Rao, G. P. « Adaptive prediction and predictive control [Book Reviews] ». IEEE Transactions on Automatic Control 44, no 7 (juillet 1999) : 1482–83. http://dx.doi.org/10.1109/tac.1999.774128.

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Flor Unda, Omar. « Adaptive control systems for solar collectors ». Athenea 2, no 4 (15 juin 2021) : 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.

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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264. [2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September. [3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344. [4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall. [5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico. [6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141. [7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266. [8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática. [9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.
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Geng, Tao, et Jin Zhao. « Adaptive Cascade Generalized Predictive Control ». International Journal of Intelligence Science 04, no 03 (2014) : 70–79. http://dx.doi.org/10.4236/ijis.2014.43009.

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Wang, Wei, et Rolf Henriksen. « Direct adaptive generalized predictive control ». Modeling, Identification and Control : A Norwegian Research Bulletin 14, no 4 (1993) : 181–91. http://dx.doi.org/10.4173/mic.1993.4.1.

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MeVay, A. C. H., et R. Sarpeshkar. « Predictive comparators with adaptive control ». IEEE Transactions on Circuits and Systems II : Analog and Digital Signal Processing 50, no 9 (septembre 2003) : 579–88. http://dx.doi.org/10.1109/tcsii.2003.815026.

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Thèses sur le sujet "Predictive and Adaptive Control"

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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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MeVay, Alex C. H. (Alex Craige Haviland) 1979. « Predictive comparators with adaptive control ». Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29654.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (p. 72).
A linear predictor and adaptive control loop are added to a conventional comparator to greatly reduce the delay. A linear predictor feeds an estimated future signal to the comparator to compensate for the comparator's internal delay. On a cycle-by-cycle basis, an adaptive controller adjusts the comparator's bias current to null the error. Emphasis is placed on low power consumption, including the development of a linear predictor with no static power consumption. Improvements of two orders of magnitude in power-delay product are demonstrated. The adaptive comparator is ideally suited for applications such as synchronous rectification but will also find broad applicability anywhere an asynchronous comparator is required, such as sensor interfaces, oscilloscope triggers, and some types of analog-digital converters.
by Alex C.H. MeVay.
M.Eng.
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Brodie, K. A. « Inferential predictive control ». Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310173.

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Elshafei, Abdel-Latif. « Adaptive predictive control : analysis and expert implementation ». Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30802.

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A generalized predictive controller has been derived based on a general state-space model. The case of a one-step control horizon has been analyzed and its equivalence to a perturbation problem has been emphasized. In the case of a small perturbation, the closed-loop poles have been calculated with high accuracy. For the case of a general perturbation, an upper bound on the permissible perturbation norm has been derived. A functional analysis approach has also been adopted to assess the closed-loop stability in the case of nonlinear systems. Both the plant-model match and plant-model mismatch cases have been analyzed. The proposed controller has proven to be so robust that an adaptive implementation based on Laguerre-filter modelling has been motivated. Both SISO and MIMO schemes have been analyzed. Using a sufficient number of Laguerre filters for modelling, the adaptive controller has been proven to be globally convergent. For low-order models, the robustness of the adaptive controller can be insured by increasing the prediction horizon. The convergence and robustness results have been extended to other predictive controllers. A comparative study has shown that the proposed controller would be superior to the other predictive controllers if the open-loop system is stable, well-damped, and of unknown order or time delay. To achieve a reliable control without deep user involvement, the adaptive version of the proposed controller has been implemented using the expert shell, G2. The resulting expert system has been used to orchestrate the operation of the controller, provide an interactive user interface, adjust the Laguerre-filter model using AI search algorithms, and evaluate the performance of the controller on-line. Based on the performance evaluation, the tuning parameters of the controller can be adjusted on-line using fuzzy-logic rules.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Fun, Wey. « Adaptive motor control using predictive neural networks ». Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/31065.

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Eure, Kenneth W. II. « Adaptive Predictive Feedback Techniques for Vibration Control ». Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30342.

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In this dissertation, adaptive predictive feedback control is used to suppress plate vibrations. The adaptive predictive controller consists of an on-line identification technique coupled with a control scheme. Various system identification techniques are investigated and implemented including batch least squares, projection algorithm, and recursive least squares. The control algorithms used include Generalized Predictive Control and Deadbeat Predictive Control. This dissertation combines system identification and control to regulate broadband disturbances in modally-dense structures. As it is assumed that the system to be regulated is unknown or time varying, the control schemes presented in this work have the ability to identify and regulate a plant with only an initial estimate of the system order. In addition, theoretical development and experimental results presented in this work confirm the fact that an adaptive controller operating in the presence of disturbances will automatically incorporate an internal noise model of the disturbance perturbing the plant if the system model order is chosen sufficiently large. It is also shown that the adaptive controller has the ability to track changes in the disturbance spectrum as well as track a time varying plant under certain conditions. This work presents a broadband multi-input multi-output control scheme which utilizes both the DSP processor and the PC processor in order to handle the computational demand of broadband regulation of a modally-dense plant. Also, the system identification technique and the control algorithm may be combined to produce a direct adaptive control scheme which estimates the control parameters directly from input and output data. Experimental results for various control techniques are presented using an acoustic plant, a rectangular plate with clamped boundary conditions, and a time varying plate.
Ph. D.
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Sheth, Katha Janak. « Model predictive control for adaptive digital human modeling ». Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/884.

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We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.
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Peng, Youbin. « On adaptive control :Pole-zero placement control and generalized predictive control ». Doctoral thesis, Universite Libre de Bruxelles, 1991. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/213050.

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Lambert, Martin Richard. « Adaptive control of flexible systems ». Thesis, University of Oxford, 1987. http://ora.ox.ac.uk/objects/uuid:d19d44f9-b7db-4b00-95be-4cf897450836.

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This thesis reports the successful application of the recently introduced Generalised Predictive Control self-tuner to the high-performance positioning of a real flexible single-link robot arm. The large amount of experimental time available on this high bandwidth system allowed exhaustive testing of the 'tuning-knobs' and 'design-filters' available to the user for tailoring the closed-loop. Based upon these experiments a coherent philosophy for configuring GPC in practice is generated. Configuration details found to be necessary for satisfactory GPC control of this high-order neutrally stable and non-minimum-phase plant, with its lightly damped resonant modes, are isolated. In particular it is found that band-pass filtering of data is essential for stable offset-free control using finite-order models of the plant. These aspects are considered in detail both theoretically and experimentally. In this application, as is often the case in practice, some information about the plant dynamics is available beforehand. Novel methods for the inclusion of this prior knowledge are introduced and their beneficial effects on the convergence of the recursive least squares estimation scheme, upon which most self-tuners are based, are demonstrated in simulation and experiment. A novel high-speed 68010/20 multi-processor computer system is described which allows the implementation of GPC at the required high sample rate (60Hz). The software division of the self-tuning algorithm into concurrently and sequentially executing tasks is discussed in detail.
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Brugnolli, Mateus Mussi. « Predictive adaptive cruise control in an embedded environment ». Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-24092018-151311/.

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The development of Advanced Driving Assistance Systems (ADAS) produces comfort and safety through the application of several control theories. One of these systems is the Adaptive Cruise Control (ACC). In this work, a distribution of two control loops of such system is developed for an embedded application to a vehicle. The vehicle model was estimated using the system identification theory. An outer loop control manages the radar data to compute a suitable cruise speed, and an inner loop control aims for the vehicle to reach the cruise speed given a desired performance. For the inner loop, it is used two different approaches of model predictive control: a finite horizon prediction control, known as MPC, and an infinite horizon prediction control, known as IHMPC. Both controllers were embedded in a microcontroller able to communicate directly with the electronic unit of the vehicle. This work validates its controllers using simulations with varying systems and practical experiments with the aid of a dynamometer. Both predictive controllers had a satisfactory performance, providing safety to the passengers.
A inclusão de sistemas avançados para assistência de direção (ADAS) tem beneficiado o conforto e segurança através da aplicação de diversas teorias de controle. Um destes sistemas é o Sistema de Controle de Cruzeiro Adaptativo. Neste trabalho, é usado uma distribuição de duas malhas de controle para uma implementação embarcada em um carro de um Controle de Cruzeiro Adaptativo. O modelo do veículo foi estimado usando a teoria de identificação de sistemas. O controle da malha externa utiliza dados de um radar para calcular uma velocidade de cruzeiro apropriada, enquanto o controle da malha interna busca o acionamento do veículo para atingir a velocidade de cruzeiro com um desempenho desejado. Para a malha interna, é utilizado duas abordagens do controle preditivo baseado em modelo: um controle com horizonte de predição finito, e um controle com horizonte de predição infinito, conhecido como IHMPC. Ambos controladores foram embarcados em um microcontrolador capaz de comunicar diretamente com a unidade eletrônica do veículo. Este trabalho valida estes controladores através de simulações com sistemas variantes e experimentos práticos com o auxílio de um dinamômetro. Ambos controladores preditivos apresentaram desempenho satisfatório, fornecendo segurança para os passageiros.
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Livres sur le sujet "Predictive and Adaptive Control"

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Engineers, Institution of Electrical, dir. Adaptive prediction and predictive control. Stevenage : P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Kanjilal, P. P. Adaptive prediction and predictive control. Stevenage, Herts., U.K : P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Mosca, E. Optimal, predictive, and adaptive control. Englewood Cliffs, N.J : Prentice Hall, 1995.

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Optimal, predictive, and adaptive control. Englewood Cliffs, N.J : Prentice Hall, 1995.

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Sánchez, Juan Manuel Martín. Adaptive predictive control : From the concepts to plant optimization. London : Prentice Hall, 1996.

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1952-, Sin Kwai Sang, dir. Adaptive filtering prediction and control. Mineola, N.Y : Dover, 2009.

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Linear prediction theory : A mathematical basis for adaptive systems. Berlin : Springer-Verlag, 1990.

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Safak, Erdal. Analysis of recordings in structural engineering : Adaptive filtering, prediction, and control. [Denver, Colo.?] : Dept. of the Interior, U.S. Geological Survey, 1988.

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Strobach, Peter. Linear Prediction Theory : A Mathematical Basis for Adaptive Systems. Berlin, Heidelberg : Springer Berlin Heidelberg, 1990.

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Camacho, E. F., et C. Bordons. Model Predictive control. London : Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5.

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Chapitres de livres sur le sujet "Predictive and Adaptive Control"

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Lann, M. V., M. Cabassud et G. Casamatta. « Adaptive Model Predictive Control ». Dans Methods of Model Based Process Control, 427–57. Dordrecht : Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0135-6_17.

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Lemos, João M., Rui Neves-Silva et José M. Igreja. « Nonlinear Adaptive Predictive Control ». Dans Adaptive Control of Solar Energy Collector Systems, 131–50. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06853-4_5.

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Filatov, Nikolai M., et Heinz Unbehauen. « 14. DUAL MODIFICATION OF PREDICTIVE ADAPTIVE CONTROLLERS ». Dans Adaptive Dual Control, 160–65. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39994-0_14.

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Sunan, Huang, Tan Kok Kiong et Lee Tong Heng. « Adaptive Predictive Control of a Class of SISO Non-Linear Systems ». Dans Applied Predictive Control, 181–206. London : Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3725-2_7.

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DeHaan, Darryl, Martin Guay et Veronica Adetola. « Adaptive Robust MPC : A Minimally-Conservative Approach ». Dans Nonlinear Model Predictive Control, 55–67. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_4.

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Landau, Ioan Doré, Rogelio Lozano, Mohammed M’Saad et Alireza Karimi. « Adaptive Prediction ». Dans Adaptive Control, 193–203. London : Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-664-1_6.

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Landau, I. D., R. Lozano et M. M’Saad. « Adaptive Prediction ». Dans Adaptive Control, 195–207. London : Springer London, 1998. http://dx.doi.org/10.1007/978-0-85729-343-5_6.

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Martín-Sánchez, Juan M., et José Rodellar. « Basic Strategy of Predictive Control ». Dans ADEX Optimized Adaptive Controllers and Systems, 67–81. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09794-7_3.

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Martín-Sánchez, Juan M., et José Rodellar. « Extended Strategy of Predictive Control ». Dans ADEX Optimized Adaptive Controllers and Systems, 83–110. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09794-7_4.

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Naus, Gerrit J. L., Jeroen Ploeg, M. J. G. Van de Molengraft, W. P. M. H. Heemels et Maarten Steinbuch. « A Model Predictive Control Approach to Design a Parameterized Adaptive Cruise Control ». Dans Automotive Model Predictive Control, 273–84. London : Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_17.

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Actes de conférences sur le sujet "Predictive and Adaptive Control"

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Fowler, Julia, Rebecca M. Jensen-Clem et Maaike A. M. van Kooten. « Battle of the Predictive Wavefront Controls : Comparing Data and Model-Driven Predictive Control for High Contrast Imaging ». Dans Adaptive Optics Systems VIII, sous la direction de Dirk Schmidt, Laura Schreiber et Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2629521.

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Swanson, Robin, Masen P. Lamb, Suresh Sivanandam, Carlos M. Correia et Kiriakos N. Kutulakos. « Predictive AO control with convolutional neural networks ». Dans Adaptive Optics Systems VII, sous la direction de Dirk Schmidt, Laura Schreiber et Elise Vernet. SPIE, 2020. http://dx.doi.org/10.1117/12.2561175.

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Abu-Ayyad, Ma’moun, Rickey Dubay et Bambang Pramujati. « A New Adaptive Generalized Predictive Control Algorithm for Nonlinear Processes ». Dans ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37113.

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This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems. This method is termed adaptive generalized predictive control which uses a multi-dimensional surface of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The adaptive generalized predictive controller was tested in simulation and its control performance compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results both demonstrate that the adaptive generalized predictive controller demonstrated improved closed-loop performance. The formulation of the nonlinear surface provides the mechanism for the adaptive approach to be readily applied to other advanced control strategies making the methodology generic.
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Bottasso, Carlo L., Roberto Nicastro, Barbara Savini et Luca Riviello. « Adaptive Reference-Augmented Predictive Control ». Dans 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282331.

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Xiuying, Cheng, Geng Tao, Liu Hengzhi et Zhao Jin. « Adaptive cascade generalized predictive control ». Dans 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531035.

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Wang, Wei, et Rolf Henriksen. « Direct Adaptive Generalized Predictive Control ». Dans 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792566.

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Vogele, Ulrich, Johannes Ziegmann et Christian Endisch. « Driver adaptive predictive velocity control ». Dans 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317668.

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Kural, Emre, et Bilin Aksun Guvenc. « Model Predictive Adaptive Cruise Control ». Dans 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642478.

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Yeo, Y. K., et D. C. Williams. « Adaptive Bilinear Model Predictive Control ». Dans 1986 American Control Conference. IEEE, 1986. http://dx.doi.org/10.23919/acc.1986.4789155.

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Gu, Xing Yuan, Wei Wang et Ming Ming Liu. « Multivariable Generalized Predictive Adaptive Control ». Dans 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791684.

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Rapports d'organisations sur le sujet "Predictive and Adaptive Control"

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones et K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), septembre 1994. http://dx.doi.org/10.2172/10178912.

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O'Brien, James G., Emily L. Barrett, Xiaoyuan Fan, Ruisheng Diao, Renke Huang et Qiuhua Huang. Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls. Office of Scientific and Technical Information (OSTI), novembre 2017. http://dx.doi.org/10.2172/1580707.

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Nishira, Hikaru, Yoji Seto, Yoshinori Yamamura et Taketoshi Kawabe. Research on an Advanced Adaptive Cruise Control System Using Vehicle-to-Vehicle Communication and Vehicle Behavior Prediction. Warrendale, PA : SAE International, mai 2005. http://dx.doi.org/10.4271/2005-08-0295.

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Fan, Xiaoyuan, Renke Huang, Qiuhua Huang, Xinya Li, Emily L. Barrett, James G. O'Brien, Zhangshuan Hou, Huiying Ren, Slaven Kincic et Hongming Zhang. Adaptive RAS/SPS System Setting for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls : A Use Case for Transformative Remedial Action Scheme Tool (TRAST) : Jim Bridger RAS Evaluation and Analysis. Office of Scientific and Technical Information (OSTI), décembre 2019. http://dx.doi.org/10.2172/1604168.

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Friedlander, B. Adaptive Decentralized Control. Fort Belvoir, VA : Defense Technical Information Center, avril 1985. http://dx.doi.org/10.21236/ada160187.

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Dustafson, Donald E. Adaptive Time Series Analysis Using Predictive Inference and Entropy. Fort Belvoir, VA : Defense Technical Information Center, décembre 1987. http://dx.doi.org/10.21236/ada191858.

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Narenda, Kumpati S. Decentralized Control and Decentralized Adaptive Control. Fort Belvoir, VA : Defense Technical Information Center, juin 2006. http://dx.doi.org/10.21236/ada454926.

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Brockett, R. W. Adaptive Filtering and Control. Fort Belvoir, VA : Defense Technical Information Center, novembre 1989. http://dx.doi.org/10.21236/ada219556.

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

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Sastry, Shankar S. Adaptive and Nonlinear Control Methodologies. Fort Belvoir, VA : Defense Technical Information Center, août 1988. http://dx.doi.org/10.21236/ada200694.

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