Relevant bibliographies by topics / Multiple model adaptive control

Contents

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

Academic literature on the topic 'Multiple model adaptive control'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Multiple model adaptive control"

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Kuipers, Matthew, and Petros Ioannou. "Multiple Model Adaptive Control With Mixing." IEEE Transactions on Automatic Control 55, no. 8 (August 2010): 1822–36. http://dx.doi.org/10.1109/tac.2010.2042345.

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Yang, Xinhao, and Ze Li. "Congestion Control Based on Multiple Model Adaptive Control." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/714320.

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The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio.
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Lourenco, J. M. A., and J. M. Lemos. "Learning in switching multiple model adaptive control." IEEE Instrumentation and Measurement Magazine 9, no. 3 (June 2006): 24–29. http://dx.doi.org/10.1109/mim.2006.1637975.

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Kim, Youngchol, Sunwook Choi, and Taeshin Cho. "Multiple model adaptive control for interval systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4699–704. http://dx.doi.org/10.1016/s1474-6670(17)56801-9.

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Zhang, Yingzhao, Weicun Zhang, Xiao Wang, and Handong Li. "Multiple Model Adaptive Control of Flexible Arm." Proceedings of International Conference on Artificial Life and Robotics 24 (January 10, 2019): 391–94. http://dx.doi.org/10.5954/icarob.2019.os14-2.

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Nasr, Tamer Ahmed, Hossam A. Abdel Fattah, and Adel A. R. Hanafy. "Multiple-model adaptive control for interval plants." International Journal of Control, Automation and Systems 10, no. 1 (February 2012): 11–19. http://dx.doi.org/10.1007/s12555-012-0102-5.

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Anderson, Brian D. O., Thomas Brinsmead, Daniel Liberzon, and A. Stephen Morse. "Multiple model adaptive control with safe switching." International Journal of Adaptive Control and Signal Processing 15, no. 5 (2001): 445–70. http://dx.doi.org/10.1002/acs.684.

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Hespanha, Jo�o, Daniel Liberzon, A. Stephen Morse, Brian D. O. Anderson, Thomas S. Brinsmead, and Franky De Bruyne. "Multiple model adaptive control. Part 2: switching." International Journal of Robust and Nonlinear Control 11, no. 5 (2001): 479–96. http://dx.doi.org/10.1002/rnc.594.

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Maybeck, P. S., and R. D. Stevens. "Reconfigurable flight control via multiple model adaptive control methods." IEEE Transactions on Aerospace and Electronic Systems 27, no. 3 (May 1991): 470–80. http://dx.doi.org/10.1109/7.81428.

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He, W. G., Howard Kaufman, and Rob Roy. "Multiple Model Adaptive Control Procedure for Blood Pressure Control." IEEE Transactions on Biomedical Engineering BME-33, no. 1 (January 1986): 10–19. http://dx.doi.org/10.1109/tbme.1986.325833.

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Dissertations / Theses on the topic "Multiple model adaptive control"

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Buchstaller, Dominic. "Robust stability and performance for multiple model switched adaptive control." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/72334/.

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While the concept of switching between multiple controllers to achieve a control objective is not new, the available analysis to date imposes various structural and analytical assumptions on the controlled plant. The analysis presented in this thesis, which is concerned with an Estimation-based Multiple Model Switched Adaptive Control (EMMSAC) algorithm originating from Fisher-Jeffes (2003), Vinnicombe (2004), is shown not to have such limitations. As the name suggests, the key difference between EMMSAC and common multiple model type switching schemes is that the switching decision is based on the outcome of an optimal estimation process. The use of such optimal estimators is the key that allows for a simplified, axiomatic approach to analysis. Also, since estimators may be implemented by standard optimisation techniques, their construction is feasible for a broad class of systems. The presented analysis is the first of its kind to provide comprehensive robustness and performance guarantees for a multiple model control algorithm, in terms of $l_p,\ 1\le p\le \infty$ bounds on the closed loop gain, and is applicable to the class of minimal MIMO LTI plants. A key feature of this bound is that it permits the on-line alteration of the plant model set (dynamic EMMSAC) in contrast to the usual assumption that the plant model set is constant (static EMMSAC). It is shown that a static EMMSAC algorithm is conservative whereas a dynamic EMMSAC algorithm, based on the technique of dynamically expanding the plant model set, can be universal. It is also shown that the established gain bounds are invariant to a refinement of the plant model set, e.g. as a successive increasing fidelity sampling of a continuum of plants. Dynamic refinement of the plant model set is considered with the view to increase expected performance. Furthermore, the established bounds --- which are also a measure of performance --- have the property that they are explicit in the free variables of the algorithm. It is shown that this property of the bound forms the basis for a principled, performance-orientated approach to design. Explicit, performance-orientated design examples are given and the trade off between dynamic and static constructions of plant model sets are investigated with respect to prior information on the acting disturbances and the uncertainty.
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Wang, Yu. "Adaptive control and learning using multiple models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783473.

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Adaptation can have different objectives. Compared to a learning behavior, which is mainly to optimize the rewards/experience obtained through the learning process, adaptive control is a type of adaptation that follows a specific target guided by a controller. Although the targets may be different, the two types of adaption share common research interests.

One of the popular research techniques for studying adaptation is the use of multiple models, where the system will utilize information from multiple environment observers instead of one to improve the adaptation behavior in terms of stability, speed and accuracy. In this thesis, applications of multiple models for two types of adaptation, adaptive control and learning, will be investigated separately. For adaptive control, the research focuses on second-level adaptation, which is a new multiple-model-based approach; for learning, the multiple model concept is designed and embedded into a type of reinforcement scheme: learning automata.

The stability, robustness and performance of second-level adaptation will be first investigated in the context of various environments, including time-varying plants and noisy disturbances. Then, a new design of second-level adaptation for general systems and input-output accessible systems will be discussed. The reasons for the improved performance using second-level adaptation are analyzed theoretically. The second part of the thesis contributes to a new method of learning automata using multiple models. The method is first applied to a two-state (binary) reward environment in the simplest case, and it is later extended to the feed-forward case when multiple states or actions are presented. Finally, general reinforcement learning automata for network cases will be discussed. In all cases, simulation studies are given, wherever appropriate, to demonstrate the improvement in performance compared to conventional approaches.

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Brend, O. "Implementation and experimental evaluation of multiple model switched adaptive control for FES-based rehabilitation." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/364612/.

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Functional electrical stimulation (FES) is a well-established approach that is employed as a therapeutic tool for the restoration of motor control in individuals experiencing muscle impairment. Although its use as a rehabilitation tool is validated by clinical results, current control approaches limit the full exploitation of its potential due to the lack of accuracy with which the FES is applied. Research has thus focused on the use of advanced, closed-loop control algorithms to provide more accurate FES that is both task-oriented, and matches the rehabilitation needs of the patient. Experimental results have been reported for a variety of control schemes. However, the majority of approaches have failed to transfer to clinical practice due to the difficulties associated with identifying a model of electrically stimulated muscle that adapts as the true plant varies with time. Estimation-based multiple model switched adaptive control (EMMSAC) is a robust control approach that has the potential to overcome the problems associated with the uncertain, time-varying properties of electrically stimulated muscle. EMMSAC utilises optimal disturbance estimation to assess the respective performances of a set of candidate plant models. Then the controller associated with the model that has best performance is switched into closed-loop operation. This thesis details the algorithmic modifications that allow disturbance estimation to be performed in the time-varying setting for nonlinear Hammerstein structures. Then it is shown experimentally that a general plant model set can be identified that represents the time-varying, FES-induced muscle activation dynamics for the population of younger healthy adults. This finding is exploited to design an EMMSAC controller that achieves accurate trajectory tracking for multiple participants with minimal prior model identification. Results indicate that the use of EMMSAC reduces RMS tracking error when compared with a fixed controller; similar results are also reported for older healthy participants. Furthermore, initial results for a small sample of stroke-participants are shown, which confirms the potential for the proposed control approach to be applied in a clinical setting for FES-based rehabilitation.
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Wang, Xiaoru. "Multi-Core Implementation of F-16 Flight Surface Control System Using GA Based Multiple Model Reference Adaptive Control Algorithm." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302130339.

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Kamalasadan, Sukumar. "A New Generation of Adaptive Control: An Intelligent Supervisory Loop Approach." University of Toledo / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1087223752.

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Sova, Václav. "Adaptivní řízení elektromechanických aktuátorů s využitím dopředného kompenzátoru založeného na více-modelovém přístupu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-391815.

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This thesis deals with the derivation of novel adaptive feed forward compensator, which will be used for the control of the electromechanical actuators used in automotive industry. The electromechnical actuators are an electronic throttle valve and an EGR valve. The introduced adaptive compensator is derived from an existing multiple model feedback control method. This work describes the derivation of this method and simulation and experimental verification. In addition, the most well known digital filter differentiators are presented and summarized in this paper because the feed forward compensator needs them for its operation. From these filters, one specific is chosen, whoose coefficients for the specific setting leads to integer multiplication and an integer implementation of the filter. This will be used to implement this filter to the FPGA and then we prove, that this implementation saves a lot of FPGA resources compared to filters implemented using fixed or floating-point arithmetic.
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Choi, Jinbae. "Closed-Loop Optimal Control of Discrete-Time Multiple Model Linear Systems with Unknown Parameters." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1441178373.

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Ni, Lingli. "Fault-Tolerant Control of Unmanned Underwater Vehicles." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28187.

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Unmanned Underwater Vehicles (UUVs) are widely used in commercial, scientific, and military missions for various purposes. What makes this technology challenging is the increasing mission duration and unknown environment. It is necessary to embed fault-tolerant control paradigms into UUVs to increase the reliability of the vehicles and enable them to execute and finalize complex missions. Specifically, fault-tolerant control (FTC) comprises fault detection, identification, and control reconfiguration for fault compensation. Literature review shows that there have been no systematic methods for fault-tolerant control of UUVs in earlier investigations. This study presents a hierarchical methodology of fault detection, identification and compensation (HFDIC) that integrates these functions systematically in different levels. The method uses adaptive finite-impulse-response (FIR) modeling and analysis in its first level to detect failure occurrences. Specifically, it incorporates a FIR filter for on-line adaptive modeling, and a least-mean-squares (LMS) algorithm to minimize the output error between the monitored system and the filter in the modeling process. By analyzing the resulting adaptive filter coefficients, we extract the information on the fault occurrence. The HFDIC also includes a two-stage design of parallel Kalman filters in levels two and three for fault identification using the multiple-model adaptive estimation (MMAE). The algorithm activates latter levels only when the failure is detected, and can return back to the monitoring loop in case of false failures. On the basis of MMAE, we use multiple sliding-mode controllers and reconfigure the control law with a probability-weighted average of all the elemental control signals, in order to compensate for the fault. We validate the HFDIC on the steering and diving subsystems of Naval Postgraduate School (NPS) UUVs for various simulated actuator and/or sensor failures, and test the hierarchical fault detection and identification (HFDI) with realistic data from at-sea experiment of the Florida Atlantic University (FAU) Autonomous Underwater Vehicles (AUVs). For both occasions, we model actuator and sensor failures as additive parameter changes in the observation matrix and the output equation, respectively. Simulation results demonstrate the ability of the HFDIC to detect failures in real time, identify failures accurately with a low computational overhead, and compensate actuator and sensor failures with control reconfiguration. In particular, verification of HFDI with FAU data confirms the performance of the fault detection and identification methodology, and provides important information on the vehicle performance.
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Pinguet, Jérémy. "Contribution à la synthèse de contrôleurs neuronaux robustes par imitation." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG002.

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Cette thèse s'intéresse à l'élaboration de systèmes de contrôle par imitation de comportements ou de décisions répondant à des exigences complexes. L'objectif est de réaliser l'apprentissage d'un contrôleur neuronal de façon efficace et robuste sur une base de données regroupant ces comportements.L'approche retenue unifie les outils de la commande robuste avec ceux de la modélisation par réseaux de neurones. Des méthodes d'identification de systèmes dynamiques sont tout d'abord développées selon des structures neuronales en cohésion avec les représentations de systèmes linéaires à paramètres variants. L'accès à ce domaine d'étude ouvre alors la voie à l'analyse de stabilité et de performance de ces types de modèles neuronaux.Les travaux proposent par la suite d'exploiter ces propriétés afin de répondre aux enjeux de robustesse inhérents à l'apprentissage de lois de commande. La méthode d'identification de contrôleurs robustes qui est proposée, repose sur l'évaluation des marges de stabilité de l'asservissement neuronal. Il est alors permis de consolider la robustesse des contrôleurs à travers une stratégie d'apprentissage avec optimisation de la stabilité par une formulation multi-objectifs. En complément, le déploiement des contrôleurs est effectué selon une méthode de contrôle adaptative multi-modèle.La démarche est finalement appliquée aux pilotes automatiques d'avion via une co-simulation avec un simulateur de vol caractérisé par sa grande fiabilité de modélisation. Les problématiques de contrôle abordées sont, dans un premier temps de guider l'appareil selon un cap et une altitude donnés, tandis qu'une seconde expérimentation se concentre sur le suivi d'un chemin de vol constitué d'une série de points de passage. Les autopilotes neuronaux sont développées par l'imitation d'un autopilote existant puis par l'imitation d'un pilote
This thesis focuses on developing control systems by imitating behaviors or decisions meeting complex requirements. The objective is to perform the learning of a neural controller efficiently and robustly on a database containing these behaviors.The chosen approach unifies robust control tools with those of neural network modeling. Methods for identifying dynamic systems are first developed according to neural structures in cohesion with the representations of linear systems with varying parameters. Access to this field of study opens the way to stability and performance analysis of these neural models.The work then proposes to exploit these properties to address the robustness issues inherent to the learning of control laws. The proposed method of identifying robust controllers is based on evaluating the stability margins of the neural feedback loop. It is then possible to consolidate the robustness of the controllers through a learning strategy with stability optimization by a multi-objective formulation. In addition, the deployment of the controllers is performed using a multi-model adaptive control method.The approach is finally applied to aircraft autopilots via a co-simulation with a flight simulator characterized by its high modeling reliability. The control issues addressed are, in the first step, to guide the aircraft according to a given heading and altitude, while a second experiment focuses on following a flight path consisting of a series of waypoints. The neural autopilots are developed by imitating an existing autopilot and then by imitating a pilot
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Gendron, Sylvain. "Model weighting adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0007/NQ44437.pdf.

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Books on the topic "Multiple model adaptive control"

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Jeon, Jung Taek. Multiple model adaptive control with application to DemoDICE. [Downsview, Ont.]: University of Toronto, Institute for Aerospace Studies, 2002.

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Jeon, Jung Taek. Multiple model adaptive control with application to DemoDICE. Ottawa: National Library of Canada, 2002.

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Datta, Aniruddha. Adaptive Internal Model Control. London: Springer London, 1998. http://dx.doi.org/10.1007/978-0-85729-331-2.

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Nguyen, Nhan T. Model-Reference Adaptive Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-56393-0.

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Adaptive internal model control. London: Springer, 1998.

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Goodwin, Graham, ed. Model Identification and Adaptive Control. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0711-8.

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Model reference adaptive control of manipulators. Taunton, Somerset, England: Research Studies Press, 1990.

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United States. National Aeronautics and Space Administration., ed. Model reference adaptive control of robots. Troy, N.Y: Rensselaer Polytechnic Institute, Electrical, Computer and Systems Engineering, 1991.

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Roderick, Murray-Smith, and Johansen Tor Arne, eds. Multiple model approaches to modelling and control. London: Taylor & Francis, 1997.

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Zourntos, Takis. Nonlinear adaptive control based on the related model. Ottawa: National Library of Canada, 1996.

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Book chapters on the topic "Multiple model adaptive control"

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Lemos, João M., Rui Neves-Silva, and José M. Igreja. "Multiple Model Adaptive Control." In Adaptive Control of Solar Energy Collector Systems, 105–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06853-4_4.

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Schott, Kevin D., and B. Wayne Bequette. "Multiple Model Adaptive Control (MMAC)." In Nonlinear Model Based Process Control, 33–57. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5094-1_2.

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Fabri, Simon G., and Visakan Kadirkamanathan. "Multiple Model Dual Adaptive Control of Spatial Multimodal Systems." In Communications and Control Engineering, 213–41. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0319-6_10.

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Fabri, Simon G., and Visakan Kadirkamanathan. "Multiple Model Dual Adaptive Control of Jump Nonlinear Systems." In Communications and Control Engineering, 187–212. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0319-6_9.

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Li, Jiayi, and Weicun Zhang. "Research on Weighted Multiple Model Adaptive Control Based on U-Model." In Lecture Notes in Electrical Engineering, 217–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8450-3_23.

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Narendra, Kumpati S., Osvaldo A. Driollet, and Koshy George. "Adaptive Control Using Multiple Models: A Methodology." In Modeling, Control and Optimization of Complex Systems, 83–110. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1139-7_5.

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Zhang, Weicun, and Qing Li. "Stable Weighted Multiple Model Adaptive Control of Continuous-Time Plant." In Virtual Equivalent System Approach for Stability Analysis of Model-based Control Systems, 111–27. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5538-1_6.

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Zhang, Yuzhen, Weicun Zhang, and Jiqiang Wang. "New Progress on Research of Weighted Multiple Model Adaptive Control." In Lecture Notes in Electrical Engineering, 167–76. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_17.

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Ji, Zhicheng, Rongjia Zhu, and Yanxia Shen. "Fuzzy Multiple Reference Models Adaptive Control Scheme Study." In Advances in Machine Learning and Cybernetics, 387–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_41.

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Zhang, Weicun, and Qing Li. "Stable Weighted Multiple Model Adaptive Control of Discrete-Time Stochastic Plant." In Virtual Equivalent System Approach for Stability Analysis of Model-based Control Systems, 65–87. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5538-1_4.

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Conference papers on the topic "Multiple model adaptive control"

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Burakov, M. V. "Fuzzy Multiple Model Adaptive Control." In 2019 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). IEEE, 2019. http://dx.doi.org/10.1109/weconf.2019.8840624.

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Baar, Tamas, Bence Beke, Peter Bauer, Balint Vanek, and Jozsef Bokor. "Smoothed multiple model adaptive estimation." In 2016 European Control Conference (ECC). IEEE, 2016. http://dx.doi.org/10.1109/ecc.2016.7810442.

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Chang Tan, Gang Tao, and Ruiyun Qi. "Direct adaptive multiple-model control schemes." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580603.

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Liu, Jiazhen, Zhenlei Wang, Xin Wang, Dahai Wang, and Feng Qian. "Multiple models robust adaptive control with reduced model." In 2010 8th IEEE International Conference on Control and Automation (ICCA). IEEE, 2010. http://dx.doi.org/10.1109/icca.2010.5524307.

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Rajagopal, A., and K. Krishnamurthy. "Adaptive Control Using Multiple Models and Model Weighting." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0413.

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Abstract A general methodology for the identification and control of dynamical systems with several operating environments and possessing a high degree of uncertainty is presented. Neural networks are used to create multiple models to capture the dynamics of the various environments of the system. Control is effected by combining these models by using an evolutionary strategy. The methodology is applied to the problem of controlling a two-link robotic manipulator in the presence of disturbances and varying load conditions. Simulated results presented show that the proposed methodology yields better results compared to the ones obtained by using a single model or by using multiple models but switching to and tuning the model with the smallest tracking error.
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Knoebel, Nathan, and Jovan Boskovic. "Model Selection Analysis in Multiple Model Adaptive Control." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-6063.

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Wang, Ya, Zhuo Kong, and Baoyong Zhao. "Multiple-Model Adaptive Control - Disturbance Rejection Study." In 2015 International Conference on Electromechanical Control Technology and Transportation. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icectt-15.2015.8.

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Maybeck, P. S., and R. D. Stevens. "Reconfigurable flight control via multiple model adaptive control methods." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203417.

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Lourenco, J. M., and J. M. Lemos. "On-line model bank enlargement for multiple model adaptive control." In European Control Conference 2007 (ECC). IEEE, 2007. http://dx.doi.org/10.23919/ecc.2007.7068734.

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Fisher, K. A., and P. S. Maybeck. "Multiple model adaptive estimation with filter spawning." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.878595.

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Reports on the topic "Multiple model adaptive control"

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

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Candy, J. Model Reference Adaptive Control (MRAC) for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1798434.

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Durling, Mike. Direct Model Reference Adaptive Control for a Magnetic Bearing. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/767405.

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Morris, Nancy M., William B. Rouse, and Paul R. Frey. Adaptive Aiding for Symbiotic Human-Computer Control: Conceptual Model and Experimental Approach. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada153870.

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Eguchi, Hiroaki, Takanori Fukao, and Koichi Osuka. Design Method of Reference Model for Active Steering Based on Nonlinear Adaptive D* Control. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0423.

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Hardt, D. E., E. Papadpoulos, and A. Suzuki. Study of reduced order model reference adaptive control systems for improved process control. Final report, May 1, 1982-January 31, 1986. Office of Scientific and Technical Information (OSTI), February 1986. http://dx.doi.org/10.2172/6114568.

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Heinz, Kevin, Itamar Glazer, Moshe Coll, Amanda Chau, and Andrew Chow. Use of multiple biological control agents for control of western flower thrips. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7613875.bard.

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The western flower thrips (WFT), Frankliniella occidentalis (Pergande), is a serious widespread pest of vegetable and ornamental crops worldwide. Chemical control for Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) on floriculture or vegetable crops can be difficult because this pest has developed resistance to many insecticides and also tends to hide within flowers, buds, and apical meristems. Predatory bugs, predatory mites, and entomopathogenic nematodes are commercially available in both the US and Israel for control of WFT. Predatory bugs, such as Orius species, can suppress high WFT densities but have limited ability to attack thrips within confined plant parts. Predatory mites can reach more confined habitats than predatory bugs, but kill primarily first-instar larvae of thrips. Entomopathogenic nematodes can directly kill or sterilize most thrips stages, but have limited mobility and are vulnerable to desiccation in certain parts of the crop canopy. However, simultaneous use of two or more agents may provide both effective and cost efficient control of WFT through complimentary predation and/or parasitism. The general goal of our project was to evaluate whether suppression of WFT could be enhanced by inundative or inoculative releases of Orius predators with either predatory mites or entomopathogenic nematodes. Whether pest suppression is best when single or multiple biological control agents are used, is an issue of importance to the practice of biological control. For our investigations in Texas, we used Orius insidiosus(Say), the predatory mite, Amblyseius degeneransBerlese, and the predatory mite, Amblyseius swirskii(Athias-Henriot). In Israel, the research focused on Orius laevigatus (Fieber) and the entomopathogenic nematode, Steinernema felpiae. Our specific objectives were to: (1) quantify the spatial distribution and population growth of WFT and WFT natural enemies on greenhouse roses (Texas) and peppers (Israel), (2) assess interspecific interactions among WFT natural enemies, (3) measure WFT population suppression resulting from single or multiple species releases. Revisions to our project after the first year were: (1) use of A. swirskiiin place of A. degeneransfor the majority of our predatory mite and Orius studies, (2) use of S. felpiaein place of Thripinema nicklewoodi for all of the nematode and Orius studies. We utilized laboratory experiments, greenhouse studies, field trials and mathematical modeling to achieve our objectives. In greenhouse trials, we found that concurrent releases of A.degeneranswith O. insidiosusdid not improve control of F. occidentalis on cut roses over releases of only O. insidiosus. Suppression of WFT by augmentative releases A. swirskiialone was superior to augmentative releases of O. insidiosusalone and similar to concurrent releases of both predator species on cut roses. In laboratory studies, we discovered that O. insidiosusis a generalist predator that ‘switches’ to the most abundant prey and will kill significant numbers of A. swirskiior A. degeneransif WFTbecome relatively less abundant. Our findings indicate that intraguild interactions between Orius and Amblyseius species could hinder suppression of thrips populations and combinations of these natural enemies may not enhance biological control on certain crops. Intraguild interactions between S. felpiaeand O. laevigatus were found to be more complex than those between O. insidiosusand predatory mites. In laboratory studies, we found that S. felpiaecould infect and kill either adult or immature O. laevigatus. Although adult O. laevigatus tended to avoid areas infested by S. felpiaein Petri dish arenas, they did not show preference between healthy WFT and WFT infected with S. felpiaein choice tests. In field cage trials, suppression of WFT on sweet-pepper was similar in treatments with only O. laevigatus or both O. laevigatus and S. felpiae. Distribution and numbers of O. laevigatus on pepper plants also did not differ between cages with or without S. felpiae. Low survivorship of S. felpiaeafter foliar applications to sweet-pepper may explain, in part, the absence of effects in the field trials. Finally, we were interested in how differential predation on different developmental stages of WFT (Orius feeding on WFT nymphs inhabiting foliage and flowers, nematodes that attack prepupae and pupae in the soil) affects community dynamics. To better understand these interactions, we constructed a model based on Lotka-Volterra predator-prey theory and our simulations showed that differential predation, where predators tend to concentrate on one WFT stage contribute to system stability and permanence while predators that tend to mix different WFT stages reduce system stability and permanence.
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Huang, Hui-Min. Outline of a multiple dimensional reference model architecture and a knowledge engineering methodology for intelligent systems control. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5643.

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Wegner, Michael D. Physician Provider Profiling in Brooke Army Medical Center's Internal Medicine Clinic: A Multiple Regression and Process Control Model. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada420371.

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Pruitt, Bruce, K. Killgore, William Slack, and Ramune Matuliauskaite. Formulation of a multi-scale watershed ecological model using a statistical approach. Engineer Research and Development Center (U.S.), November 2020. http://dx.doi.org/10.21079/11681/38862.

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The purpose of this special report is to provide a statistical stepwise process for formulation of ecological models for application at multiple scales using a stream condition index (SCI). Given the global variability of aquatic ecosystems, this guidance is for broad application and may require modification to suit specific watersheds or stream reaches. However, the general statistical treatise provided herein applies across physiographies and at multiple scales. The Duck River Watershed Assessment in Tennessee was used, in part, to develop and test this multiscale, statistical approach; thus, it is considered a case example and referenced throughout this report. The findings of this study can be utilized to (1) prioritize water-sheds for restoration, enhancement, and conservation; (2) plan and conduct site-specific, intensive ecosystem studies; and (3) assess ecosystem outcomes (that is, ecological lift) applicable to future with and without restoration actions including alternative, feasibility, and cost-benefit analyses and adaptive management.
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