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Journal articles on the topic 'Recursive control'

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Published: 7 July 2024

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1

Megha Agarwal and Indra Gupta. "Chip Architecture for Data Sorting Using Recursive Algorithm." Journal of Technology Management for Growing Economies 1, no. 1 (April 26, 2010): 93–102. http://dx.doi.org/10.15415/jtmge.2010.11006.

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“This paper suggests a way to implement recursive algorithm on hardware with an example of sorting of numeric data. Every recursive call/return needs a mechanism to store/restore parameters, local variables and return addresses respectively. Also a control sequence is needed to control the flow of execution as in case of recursive call and recursive return. The number of states required for the execution of a recursion in hardware can be reduced compared with software. This paper describes all the details that are required to implement recursive algorithm in hardware. For implementation, all the entities are designed using VHDL and are synthesized, configured on Spartan-2 XC2S200-5PQ208. “

2

Bobál, Vladimír, Petr Chalupa, Marek Kubalčík, and Petr Dostál. "Self-Tuning Predictive Control of Nonlinear Servo-Motor." Journal of Electrical Engineering 61, no. 6 (November 1, 2010): 365–72. http://dx.doi.org/10.2478/v10187-010-0056-x.

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Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

3

OSTOJIC, MILE. "Recursive control of robotic motion." International Journal of Control 64, no. 5 (July 1996): 775–87. http://dx.doi.org/10.1080/00207179608921656.

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4

Skiadas, Costis. "Robust control and recursive utility." Finance and Stochastics 7, no. 4 (October 1, 2003): 475–89. http://dx.doi.org/10.1007/s007800300100.

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5

Ushakov, Mariya S., and Alexander I. Legalov. "Verification of Programs with Mutual Recursion in the Pifagor Language." Modeling and Analysis of Information Systems 25, no. 4 (August 27, 2018): 358–81. http://dx.doi.org/10.18255/1818-1015-2018-4-358-381.

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In the article, we consider verification of programs with mutual recursion in the data driven functional parallel language Pifagor. In this language the program could be represented as a data flow graph, that has no control connections, and has only data relations. Under these conditions it is possible to simplify the process of formal verification, since there is no need to analyse resource conflicts, which are present in the systems with ordinary architectures. The proof of programs correctness is based on the elimination of mutual recursions by program transformation. The universal method of mutual recursion of an arbitrary number of functions elimination consists in constructing the universal recursive function that simulates all the functions in the mutual recursion. A natural number is assigned to each function in mutual recursion. The universal recursive function takes as its argument the number of a function to be simulated and the arguments of this function. In some cases of the indirect recursion it is possible to use a simpler method of program transformation, namely, the merging of the functions code into a single function. To remove mutual recursion of an arbitrary number of functions, it is suggested to construct a graph of all connected functions and transform this graph by removing functions that are not connected with the target function, then by merging functions with indirect recursion and finally by constructing the universal recursive function. It is proved that in the Pifagor language such transformations of functions as code merging and universal recursive function construction do not change the correctness of the initial program. An example of partial correctness proof is given for the program that parses a simple arithmetic expression. We construct the graph of all connected functions and demonstrate two methods of proofs: by means of code merging and by means of the universal recursive function.

6

Dai, Zhi Hua, Yu An Pan, and Jie Yao. "Parameters Recursive Identification of Minimum Variance Control." Applied Mechanics and Materials 347-350 (August 2013): 15–18. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.15.

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we discuss the problem of parameters recursive identification and designing of optimal input signal for minimum variance control from the point of system identification. we propose multi-innovation recursive least-squares identification method and separable iterative recursive least-squares identification method to identify and estimate it on line. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.

7

Siwczyński, M., and K. Hawron. "The optimal online control of the instantaneous power and the multiphase source’s current." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 6 (December 1, 2017): 827–32. http://dx.doi.org/10.1515/bpasts-2017-0090.

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AbstractThe paper presents the new optimal real-time control algorithm of the power source. The minimum of the square-instantaneous current was assumed as an optimal criterion, with an additional constraint on source instantaneous power. The mathematical model of a multiphase source was applied as a voltage-current convolution in the discrete time domain. The resulting control algorithm was the recursive digital filter with infinite recursion.

8

Ma, Baosen, Wenhui Pei, and Qi Zhang. "Trajectory Tracking Control of Autonomous Vehicles Based on an Improved Sliding Mode Control Scheme." Electronics 12, no. 12 (June 20, 2023): 2748. http://dx.doi.org/10.3390/electronics12122748.

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This paper addresses the issue of external unknown environmental interference affecting the trajectory tracking performance and driving stability of autonomous vehicles. This seriously impacts the performance and stability of the vehicle while driving. In order to provide precise, reliable, and safe trajectory tracking performance for autonomous vehicles, this paper proposes a recursive integral terminal sliding mode control (RITSMC) method. The proposed RITSMC combines the advantages of recursive integral sliding mode (RISM), terminal sliding mode (TSM), and adaptive algorithms, and can effectively achieve precise trajectory tracking and driving stability of autonomous vehicles. Furthermore, compared with traditional methods, an adaptive algorithm is introduced on the recursive sliding surface to enable real-time adaptation of the control parameters of the recursive controller, further improving the trajectory tracking accuracy and driving stability of autonomous vehicles. The stability of this control system is demonstrated by using a Lyapunov function. Finally, multiple simulation tests were conducted on different lane speeds on both wet and dry asphalt road sections. By comparing the simulation results, it was found that the proposed RITSMC exhibits excellent performance in terms of the precision of tracking trajectories and the stability of driving, in contrast to traditional sliding mode controllers (SMC) and integral terminal sliding mode controllers (ITSMC).

9

Andréasson, Sven-Arne. "A Recursive Framework for Manufacturing Control." IFAC Proceedings Volumes 42, no. 4 (2009): 600–605. http://dx.doi.org/10.3182/20090603-3-ru-2001.0372.

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10

Kreutz-Delgado, K., A. Jain, and G. Rodriguez. "Recursive Formulation of Operational Space Control." International Journal of Robotics Research 11, no. 4 (August 1992): 320–28. http://dx.doi.org/10.1177/027836499201100405.

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11

Ydstie, B. E., and T. Co. "Recursive estimation with adaptive divergence control." IEE Proceedings D Control Theory and Applications 132, no. 3 (1985): 124. http://dx.doi.org/10.1049/ip-d.1985.0022.

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12

Hu, Quan, Chuandong Guo, Yao Zhang, and Jun Zhang. "Recursive decentralized control for robotic manipulators." Aerospace Science and Technology 76 (May 2018): 374–85. http://dx.doi.org/10.1016/j.ast.2018.02.018.

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13

Nicolet, Baptiste, Fabrice Rousselle, Jan Novak, Alexander Keller, Wenzel Jakob, and Thomas Müller. "Recursive Control Variates for Inverse Rendering." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–13. http://dx.doi.org/10.1145/3592139.

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We present a method for reducing errors---variance and bias---in physically based differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as part of an optimization loop involving gradient descent. The actual change introduced by each gradient descent step is often relatively small, causing a significant degree of redundancy in this computation. We exploit this redundancy by formulating a gradient estimator that employs a recursive control variate , which leverages information from previous optimization steps. The control variate reduces variance in gradients, and, perhaps more importantly, alleviates issues that arise from differentiating loss functions with respect to noisy inputs, a common cause of drift to bad local minima or divergent optimizations. We experimentally evaluate our approach on a variety of path-traced scenes containing surfaces and volumes and observe that primal rendering efficiency improves by a factor of up to 10.

14

Park, H. M., and W. J. Lee. "Recursive Identification of Thermal Convection." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (March 1, 2003): 1–10. http://dx.doi.org/10.1115/1.1540116.

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A method is developed for the recursive identification of thermal convection system governed by the Boussinesq equation using an extended Kalman filter. A computationally feasible Kalman filter is constructed by reducing the Boussinesq equation to a small number of ordinary differential equations by means of the Karhunen-Loe`ve Galerkin procedure which is a type of Galerkin method employing the empirical eigenfunctions of the Karhunen-Loe`ve decomposition. Employing the Kalman filter constructed by using the reduced order model, the thermal convection induced by a spatially varying heat flux at the bottom is identified recursively by using either the Boussinesq equation or the reduced order model itself. The recursive identification technique developed in the present work is found to yield accurate results for thermal convection even with approximate covariance equation and noisy measurements. It is also shown that a reasonably accurate and computationally feasible method of recursive identification can be constructed even with a relatively inaccurate reduced order model.

15

Rasskazov, Sergey. "RECURSIVITY IN DIGITAL COLLABORATION PLATFORMS." Political Expertise: POLITEX 18, no. 1 (2022): 39–55. http://dx.doi.org/10.21638/spbu23.2022.103.

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This article investigates conditions of the reproduction of the techno-social component of recursivity with collective collaboration in the development and use of network information technologies. For this purpose, this article uses two cases that give a prototype of the institution of “governability without interference.” In the concept of the “recursive public” that underlies the first case, accessibility and changeability of the subject of its activity are important. Such conditions take place in the social environment of programmers who create and develop freely available software. In its scheme, passive and active recursive processes are distinguished. The first case is “consumption” of software by external stakeholders, while the second is about active participation of community members in its creation. Close to this concept are the ideas of “recursive organization,” “recursive citizenship,” and “recursive democracy” in a networked society. These are revealed in the second case using the example of the Decidim project. A distinctive feature of the digital political network that is being formed is the desire to meet basic requirements of radical democracy. It is shown that this orientation is largely associated with the ideas and experience of the social movement 15-M. Project participants, collaborating with each other, can consistently control the functioning of its digital platform and data, as well as modify other elements, including internal code and interface part. In the activities of the community that has developed in the project, the recursive process of creating organizational structures and their influence on the formation of the “action space” is highlighted. As in the first case, we can specify active and passive forms of recursion in the project. The understanding of technopolitical democratization, including the recursion mechanism, is disclosed. The process is presented in the form of an ontology schema. The results obtained concretize the understanding of recursivity and its impact on the development of digital collaboration platforms.

16

Singh, Angadh, Abhijit S. Badwe, and Sachin C. Patwardhan. "CONSTRAINED RECURSIVE PARAMETER ESTIMATION FOR ADAPTIVE CONTROL." IFAC Proceedings Volumes 40, no. 5 (2007): 279–84. http://dx.doi.org/10.3182/20070606-3-mx-2915.00094.

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17

Wei, Qingmeng, Jiongmin Yong, and Zhiyong Yu. "Time-Inconsistent Recursive Stochastic Optimal Control Problems." SIAM Journal on Control and Optimization 55, no. 6 (January 2017): 4156–201. http://dx.doi.org/10.1137/16m1079415.

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18

Wang, Kaibo, and Fugee Tsung. "Recursive parameter estimation for categorical process control." International Journal of Production Research 48, no. 5 (January 13, 2009): 1381–94. http://dx.doi.org/10.1080/00207540802570651.

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19

Hui, Eddie, Jianhui Huang, Xun Li, and Guangchen Wang. "Near-optimal control for stochastic recursive problems." Systems & Control Letters 60, no. 3 (March 2011): 161–68. http://dx.doi.org/10.1016/j.sysconle.2010.10.010.

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20

Kitamura, A., T. Takahashi, K. Nose, M. Konishi, K. Tani, and T. Nakada. "Recursive Identification Technique for Roll Eccentricity Control." IFAC Proceedings Volumes 20, no. 5 (July 1987): 113–18. http://dx.doi.org/10.1016/s1474-6670(17)55426-9.

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21

Harb, Ahmad M., Ashraf A. Zaher, Ahmad A. Al-Qaisia, and Mohammad A. Zohdy. "Recursive backstepping control of chaotic Duffing oscillators." Chaos, Solitons & Fractals 34, no. 2 (October 2007): 639–45. http://dx.doi.org/10.1016/j.chaos.2006.03.119.

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22

Iwasaki, T., R. E. Skelton, and M. Corless. "A recursive construction algorithm for covariance control." IEEE Transactions on Automatic Control 43, no. 2 (1998): 268–72. http://dx.doi.org/10.1109/9.661079.

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23

Imai, H. "Recursive estimation and control for stochastic systems." Probabilistic Engineering Mechanics 5, no. 1 (March 1990): 47. http://dx.doi.org/10.1016/0266-8920(90)90032-f.

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24

Nasri, A. H. "Boundary-corner control in recursive-subdivision surfaces." Computer-Aided Design 23, no. 6 (July 1991): 405–10. http://dx.doi.org/10.1016/0010-4485(91)90008-k.

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25

Hu, Xiaoming, Maja Karasalo, and Clyde F. Martin. "Periodic and Recursive Control Theoretic Smoothing Splines." Communications in Information and Systems 10, no. 3 (2010): 137–54. http://dx.doi.org/10.4310/cis.2010.v10.n3.a1.

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26

Hansen, Lars Peter, and Thomas J. Sargent. "Recursive robust estimation and control without commitment." Journal of Economic Theory 136, no. 1 (September 2007): 1–27. http://dx.doi.org/10.1016/j.jet.2006.06.010.

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27

Hemerly, E. M., and M. H. A. Davis. "Recursive order estimation of stochastic control systems." Mathematical Systems Theory 22, no. 1 (December 1989): 323–46. http://dx.doi.org/10.1007/bf02088305.

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28

Chanane, B. "Bilinear quadratic optimal control: a recursive approach." Optimal Control Applications and Methods 18, no. 4 (July 1997): 273–82. http://dx.doi.org/10.1002/(sici)1099-1514(199707/08)18:4<273::aid-oca601>3.0.co;2-x.

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29

de Silva, Clarence W. "Recursive linearizing and decoupling control of robots." Dynamics and Control 2, no. 4 (October 1992): 401–14. http://dx.doi.org/10.1007/bf02172224.

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30

PARKUM, J. E., N. K. POULSEN, and J. HOLST. "Recursive forgetting algorithms." International Journal of Control 55, no. 1 (January 1992): 109–28. http://dx.doi.org/10.1080/00207179208934228.

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31

Yamakita, M., T. Kubozono, and K. Furuta. "A New Recursive Form Adaptive Control of Manipulators and its Relations to Conventional Recursive-Closed Form Adaptive Controls." IFAC Proceedings Volumes 26, no. 2 (July 1993): 311–16. http://dx.doi.org/10.1016/s1474-6670(17)48739-8.

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32

Wagner, Eric G. "Algebraic Theories, Data Types, and Control Constructs." Fundamenta Informaticae 9, no. 3 (July 1, 1986): 343–70. http://dx.doi.org/10.3233/fi-1986-9305.

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The aim of this paper is to model recursive types, equational types, and elementary programming control constructs (such as conditionals and while-do) in one, comparatively simple, algebraic framework, that can be used for theoretical studies and as a basis for data type and program specification. To this end we introduce a new kind of algebraic theory, the RV-theory. We give simple examples of the use of such theories for data type specification. We provide a mathematical semantics for these specifications that extends the initial algebra semantics for equational specification to include recursive types.

33

Etessami, Kousha, and Mihalis Yannakakis. "Recursive Markov Decision Processes and Recursive Stochastic Games." Journal of the ACM 62, no. 2 (May 6, 2015): 1–69. http://dx.doi.org/10.1145/2699431.

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34

Wu, Zhen, and Feng Zhang. "Maximum Principle for Stochastic Recursive Optimal Control Problems Involving Impulse Controls." Abstract and Applied Analysis 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/709682.

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We consider a stochastic recursive optimal control problem in which the control variable has two components: the regular control and the impulse control. The control variable does not enter the diffusion coefficient, and the domain of the regular controls is not necessarily convex. We establish necessary optimality conditions, of the Pontryagin maximum principle type, for this stochastic optimal control problem. Sufficient optimality conditions are also given. The optimal control is obtained for an example of linear quadratic optimization problem to illustrate the applications of the theoretical results.

35

Антропов, Н. Р., and Е. Д. Агафонов. "KERNEL RECURSIVE DUAL CONTROL ALGORITHM FOR NONLINEAR DYNAMIC PROCESSES." СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, no. 4(86) (December 30, 2021): 73–78. http://dx.doi.org/10.36622/vstu.2021.86.4.016.

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В работе рассматривается задача адаптивной идентификации и управления нелинейными динамическими объектами. Предлагается новый рекуррентный ядерный алгоритм дуального управления с использованием идентификатора. Приводятся результаты численного исследования, подтверждающие эффективность предложенного алгоритма. The paper considers adaptive identification and control problem of nonlinear dynamic processes. For solution of the problem new recursive identification and control algorithms are proposed. The paper presents the results of a numerical study illustrating the performance of the proposed recursive identification and control algorithms.

36

Zhang, Liangquan. "Singular optimal controls for stochastic recursive systems under convex control constraint." Journal of Mathematical Analysis and Applications 497, no. 2 (May 2021): 124905. http://dx.doi.org/10.1016/j.jmaa.2020.124905.

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37

TROIANO, LUIGI, and RONALD R. YAGER. "RECURSIVE AND ITERATIVE OWA OPERATORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 06 (December 2005): 579–99. http://dx.doi.org/10.1142/s0218488505003680.

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An important issue when using the OWA aggregation operators is the determination of weights. One approach is to link the weights to a desired attitudinal character for the aggregation. The ME-OWA operators provide a pioneering example of this approach. Here we first present an alternative approach to generating OWA weights with a desired attitudinal character. We accomplish this by using a family of recursive OWA operators (R-OWA). We then generalize this with a class that allows of OWA aggregation by iteration (It-OWA). Both families are built with the constraint of keeping constant the attitudinal character at any recursion or any iteration step. This is particularly useful in aggregations that sequentially add arguments to the aggregation.

38

Stotsky, A. A. "Recursive trigonometric interpolation algorithms." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 224, no. 1 (February 2010): 65–77. http://dx.doi.org/10.1243/09596518jsce823.

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Wu, Xuan, Hui Wang, Xiaofang Yuan, Shoudao Huang, and Derong Luo. "Design and Implementation of Recursive Model Predictive Control for Permanent Magnet Synchronous Motor Drives." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/431734.

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In order to control the permanent-magnet synchronous motor system (PMSM) with different disturbances and nonlinearity, an improved current control algorithm for the PMSM systems using recursive model predictive control (RMPC) is developed in this paper. As the conventional MPC has to be computed online, its iterative computational procedure needs long computing time. To enhance computational speed, a recursive method based on recursive Levenberg-Marquardt algorithm (RLMA) and iterative learning control (ILC) is introduced to solve the learning issue in MPC. RMPC is able to significantly decrease the computation cost of traditional MPC in the PMSM system. The effectiveness of the proposed algorithm has been verified by simulation and experimental results.

40

Goldenberg, A. A., J. A. Apkarian, and H. W. Smith. "A New Approach to Kinematic Control of Robot Manipulators." Journal of Dynamic Systems, Measurement, and Control 109, no. 2 (June 1, 1987): 97–103. http://dx.doi.org/10.1115/1.3143843.

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A new approach for solving the inverse kinematics problem iteratively is presented. The solution is based on the recursive estimation of a kinematics operator which maps the task space coordinates into joint coordinates. The recursive estimation is based on least square approximation. For controlling the robot, the solution (Joint coordinates) must be compensated to achieve an arbitrarily small error (in least square sense) of the desired task space coordinates. The compensation is provided by closed loop feedback of task space coordinates using an optimal control approach.

41

Xiao, Yu Jie, Zhi Ming Qiu, and Zhang Song Shi. "Effectiveness Evaluation Model for Collaborative Command and Control System." Advanced Materials Research 981 (July 2014): 509–12. http://dx.doi.org/10.4028/www.scientific.net/amr.981.509.

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Based on the theory of information and structure entropy, this paper analyses the influence of collaborative command and control system on information flow. Two concepts, time-effect and equality of information flow, are introduced, and the time-effect quality model is established, which can be used for evaluating the order degree of collaborative command and control system. Centralized, recursive and network cooperative structure and its time and space complexity respectively discussed. Study has shown that the recursive cooperative structure under the control of the command center is a relatively good structure.

42

Baruch, Ieroham, and Edmundo P. Reynaud. "Recurrent Neural Adaptive Control of Nonlinear Oscillatory Systems Using a Complex-valued Levenberg-Marquardt Learning Algorithm." Information Technologies and Control 13, no. 1-2 (June 1, 2015): 10–24. http://dx.doi.org/10.1515/itc-2016-0007.

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Abstract In this work, a Recursive Levenberg-Marquardt learning algorithm in the complex domain is developed and applied in the training of two adaptive control schemes composed by Complex-Valued Recurrent Neural Networks. Furthermore, we apply the identification and both control schemes for a particular case of nonlinear, oscillatory mechanical plant to validate the performance of the adaptive neural controller and the learning algorithm. The comparative simulation results show the better performance of the newly proposed Complex-Valued Recursive Levenberg-Marquardt learning algorithm over the gradient-based recursive Back-propagation one.

43

SHI, Y. J., and G. F. MA. "MISSILE CONTROL DESIGN BASED ON THE LINEAR MULTIPLE SLIDING MODE RECURSIVE METHOD." ANZIAM Journal 49, no. 4 (April 2008): 573–87. http://dx.doi.org/10.1017/s1446181108000229.

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AbstractTo ensure that the elevator of a cruise missile is operating within the design specification in high-attitude flight, we present a design method for the construction of a sliding mode recursive variable structure controller. In this design method, a target sliding mode surface is first designed without considering the engineering specification of the elevator. Secondly, by using this specification, the critical state is solved. Then, the transitional sliding mode surfaces are designed recursively by using the critical state of the previous sliding mode surface so that the state will move smoothly from one transitional sliding mode surface to the next until the target sliding mode surface. This design method is based on linear sliding mode variable structure theory. Thus, the controller obtained is simple in structure and practical. Furthermore, the elevator will operate within the engineering specification. The simulation results show the effectiveness of the proposed method.

44

Singh, Piyush Pratap, Kshetrimayum Milan Singh, and Binoy Krishna Roy. "Chaos control in biological system using recursive backstepping sliding mode control." European Physical Journal Special Topics 227, no. 7-9 (October 2018): 731–46. http://dx.doi.org/10.1140/epjst/e2018-800023-6.

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45

Xing, Chao, and Ling Wang. "Recursive Adaptive Control for Solar Panel Servo System." Applied Mechanics and Materials 325-326 (June 2013): 1126–29. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1126.

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Two typical methods for model reference adaptive control are introduced. By integrating the Narendra adaptive control method and the variable structure model reference adaptive control method, a new variable model reference adaptive recursive control method is presented. The results of simulation computations show that the new method has the merits of the above two methods and is efficient and effective.

46

Harb, A. M., and M. A. Zohdy. "Chaos and Bifurcation Control Using Nonlinear Recursive Controller." Nonlinear Analysis: Modelling and Control 7, no. 2 (December 5, 2002): 37–43. http://dx.doi.org/10.15388/na.2002.7.2.15192.

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Chaos and bifurcation control is achieved by nonlinear controller that is able to mitigate the characteristics of a class of nonlinear systems that are experiencing such phenomenon. In this paper, a backstepping nonlinear recursive controller is presented. Comparison has been made between it and a Pole Placement controller. The study shows the effectiveness of the proposed control under various operating conditions.

47

Kessler, Claudius, and John Anderson. "Learning Flow of Control: Recursive and Iterative Procedures." Human-Computer Interaction 2, no. 2 (June 1, 1986): 135–66. http://dx.doi.org/10.1207/s15327051hci0202_2.

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48

Scheper, Christine M., and Robert J. Bernhard. "Modal control using the RLMS adaptive, recursive algorithm." Journal of the Acoustical Society of America 91, no. 4 (April 1992): 2349–50. http://dx.doi.org/10.1121/1.403450.

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Borkar, Vivek. "Recursive self-tuning control of finite Markov chains." Applicationes Mathematicae 24, no. 2 (1997): 169–88. http://dx.doi.org/10.4064/am-24-2-169-188.

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