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Articoli di riviste sul tema "Non-linear Control"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 maggio 2024

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1

Javadi Moghaddam, Jalal, Ghasem Zarei, Davood Momeni e Hamideh Faridi. "Non-linear control model for use in greenhouse climate control systems". Research in Agricultural Engineering 68, No. 1 (23 marzo 2022): 9–17. http://dx.doi.org/10.17221/37/2021-rae.

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In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

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2

Willis, M. J., M. T. Tham, G. A. Montague e A. J. Morris. "Non-Linear Predictive Control". IFAC Proceedings Volumes 24, n. 1 (gennaio 1991): 69–74. http://dx.doi.org/10.1016/s1474-6670(17)51298-7.

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3

Haddad, Wassim M., Vijaysekhar Chellaboina, Jerry L. Fausz e Alexander Leonessa. "Optimal non-linear robust control for non-linear uncertain systems". International Journal of Control 73, n. 4 (gennaio 2000): 329–42. http://dx.doi.org/10.1080/002071700219687.

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4

Sewell, Tazden, Jared Padayachee e Glen C. Snedden. "Non-linear AUV Controller Design Using Logic-Based Switching PID Control". International Journal of Mechanical Engineering and Robotics Research 13, n. 2 (2024): 227–40. http://dx.doi.org/10.18178/ijmerr.13.2.227-240.

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The inspection of pipes without stopping pipeline operations is of industrial interest due to the inherent economic benefits. A pipe inspection robot design is proposed together with a suitable motion controller. The proposed robot is an Autonomous Underwater Vehicle (AUV) architecture that does not require shunting, draining or unearthing pipework for inspection purposes. This posed the challenge of controlling the AUV through a narrow pipe where manouvers are restricted. A set of general non-linear equations of motion were identified and refined using existing research and strip theory. The non-linear analytical model was implemented in Simulink to enable real-time monitoring and controller tuning. A non-linear controller based on a combination of classical PID theory and switching logic was developed to control the platform. The controller was transplanted into a Hardware-in-Loop (HIL) testing model developed with the Arduino and Simulink software suites. Pool tests measuring the parameters pitch and yaw angles showed that when operated independently with a 5° input, the maximum overshoot was 0.5° or 10% of the command value with a maximum angular velocity of 1.25°/s. When operated simultaneously, the overshoot rose to 30% with a constant error in the region of 1° over the target. Distance readings conducted with ultrasonic sensors to the pipe wall showed constant Sway-Heave bias errors from the centerline as −5 mm, and 4 mm, respectively, with an error range of 4 mm / −7 mm for Sway and 6 mm/ −4 mm for Heave. Pitch and heave motions were up to 18% faster than yaw and sway motions due to actuator orientation, with speeds of 6.25°/s and 40.57 mm/s, respectively. Despite the turbulence present during tests, the controller successfully drove the AUV to target positions through active flow and presented a reasonable basis for further refinement.

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5

YOSHINE, KATSUMI, e NAOHIRO ISHII. "Non-linear analysis of a linear-non-linear-linear system". International Journal of Systems Science 23, n. 4 (aprile 1992): 623–30. http://dx.doi.org/10.1080/00207729208949235.

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6

Chapman, M. J., K. R. Godfrey, M. J. Chappell e N. D. Evans. "Structural identifiability of non-linear systems using linear/non-linear splitting". International Journal of Control 76, n. 3 (gennaio 2003): 209–16. http://dx.doi.org/10.1080/0020717031000067420.

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7

Ouyang, H., G. P. Liu, D. Rees e W. Hu. "Predictive control of networked non-linear control systems". Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, n. 3 (maggio 2007): 453–63. http://dx.doi.org/10.1243/09596518jsce271.

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8

AMMAN, HANS M., e HENK JAGER. "Constrained control algorithm for non-linear control problems". International Journal of Systems Science 19, n. 9 (gennaio 1988): 1781–93. http://dx.doi.org/10.1080/00207728808964075.

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9

Liu, Yusheng. "Robust adaptive control of uncertain non-linear systems with non-linear parameterisation". International Journal of Modelling, Identification and Control 1, n. 2 (2006): 151. http://dx.doi.org/10.1504/ijmic.2006.010091.

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10

Spurgeon, S. K. "Non-Linear Control for Uncertain Systems". Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 208, n. 4 (novembre 1994): 205–13. http://dx.doi.org/10.1243/pime_proc_1994_208_333_02.

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Abstract (sommario):

This paper presents a review of a number of state-of-the-art non-linear control design techniques which may be readily applied to solve practical problems in a robust fashion. It is first shown that non-linear controllers may be designed using straightforward linear models with minimal recourse to abstract mathematical concepts. More involved philosophies for robust control system design of inherently non-linear plants are then briefly described. A straightforward yet rigorous design framework is then presented to implement the philosophy. Tutorial examples are presented throughout the paper in order to illustrate major points of interest.

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11

Kouvaritakis, B., M. Cannon e J. A. Rossiter. "Non-linear model based predictive control". International Journal of Control 72, n. 10 (gennaio 1999): 919–28. http://dx.doi.org/10.1080/002071799220650.

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12

Duato, J., e P. Albertos. "Simplified Non-Linear Control of Lifts". IFAC Proceedings Volumes 19, n. 13 (novembre 1986): 149–54. http://dx.doi.org/10.1016/s1474-6670(17)59532-4.

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13

Veselý, V., e D. Mudronc̆ík. "Power system non-linear adaptive control". Electric Power Systems Research 22, n. 3 (dicembre 1991): 235–42. http://dx.doi.org/10.1016/0378-7796(91)90010-k.

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14

Mareels, Iven M. Y., e Robert R. Bitmead. "Non-linear dynamics in adaptive control". Automatica 24, n. 4 (luglio 1988): 485–97. http://dx.doi.org/10.1016/0005-1098(88)90093-3.

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15

Abdelbari, Slim, e Jelel Ezzine. "Non Linear Disturbance Accommodation Fuzzy Control". Journal of Advanced Computational Intelligence and Intelligent Informatics 12, n. 2 (20 marzo 2008): 165–71. http://dx.doi.org/10.20965/jaciii.2008.p0165.

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This paper deals with the problem of chaotic disturbances accommodation when these are generated by known non linear dynamics. In order to accomplish this goal, Takagi-Sugeno fuzzy models are called for as they offer the advantage of having virtually a linear rule consequent to approximate non linear systems. A control law inspired from the known disturbance accommodation control theory (DAC theory) is used to make the effects of disturbances vanish or attenuated while the considered linear plant is stabilized at the same time. An illustrative example is provided.

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16

Radhakrishnan, T. K., S. Sundaram e M. Chidambaram. "Non-linear control of continuous bioreactors". Bioprocess Engineering 20, n. 2 (1999): 173. http://dx.doi.org/10.1007/s004490050577.

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17

Park, Ji-Hun, Kyung-Won Min e Hongjin Kim. "Probabilistic bounded non-linear control algorithm for linear structures". Probabilistic Engineering Mechanics 20, n. 2 (aprile 2005): 168–78. http://dx.doi.org/10.1016/j.probengmech.2005.05.001.

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18

Patil, R. V., e A. N. Chapgaon. "Inventory Control by Linear and Non Linear Demand Forecasting". Asian Review of Mechanical Engineering 6, n. 2 (5 novembre 2017): 19–26. http://dx.doi.org/10.51983/arme-2017.6.2.2429.

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Now a day, supply chain practices are widely adopted in Indian industries .Research points out examination of success factors and implementations of the system in Indian industries. However, the adoption in Small and Medium Enterprises is not very common. Interestingly, multinational firms and large enterprises can invest huge capital for implementing latest information technology tools to share the information and carry day-to-day operations, but the investment and implementation is quite difficult for SMEs. This inspires us to investigate effect of new age supply chain technology like VMI practices in SMEs and other industries. VMI entails forecasting demand through joint efforts of customer and supplier, maintaining a targeted service level for customers, initiating and shipping supply orders, material control and customer order fulfillment.In this study, the results of adopting a partial vendor managed inventory practice, along with latest decision support tool like ANN, are presented. Outcomes of case study shows that deployment of vendor managed forecasting improves forecasting accuracy, reduces bullwhip, minimizes total supply chain cost, improves profits and most importantly improves customer satisfaction indexOverall five statistical models and five neural network models are adopted and compared. Study illustrates how a neural network aptly learns the case dynamics, and improves system performance. The results presented in this section demonstrates the effectiveness of the Focused Time Lagged Recurrent Neural Networks (FTLRNN) model compared to traditional and other neural network models. The significant finding of this research is results of forecasting error and other supply chain performance measures. Further study reveals that when we bracket the overstock and under stock cost in the supply chain cost, a forecast with minimum forecasting error may not lead to reduced supply chain cost or improved profits. This study also introduces a mixed model where the error obtained from statistical model is mixed with the forecast obtained by neural model and a new forecast is obtained. The analysis shows that the developed model could further improve supply chain performance in VMI setting.

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19

YAZ, ENGIN. "Linear state estimators for non-linear stochastic systems with noisy non-linear observations". International Journal of Control 48, n. 6 (dicembre 1988): 2465–75. http://dx.doi.org/10.1080/00207178808906341.

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20

Mollabashi, H. E., e A. H. Mazinan. "Adaptive composite non‐linear feedback‐based sliding mode control for non‐linear systems". Electronics Letters 54, n. 16 (agosto 2018): 973–74. http://dx.doi.org/10.1049/el.2018.0619.

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21

Baffi, G., J. Morris e E. Martin. "Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Least Squares". Chemical Engineering Research and Design 80, n. 1 (gennaio 2002): 75–86. http://dx.doi.org/10.1205/026387602753393240.

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22

Nakamoto, Kazuhiko, e Norihiro Watanabe. "Multivariable control experiments of non-linear chemical processes using non-linear feedback transformation". Journal of Process Control 1, n. 3 (maggio 1991): 140–45. http://dx.doi.org/10.1016/0959-1524(91)85002-z.

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23

Kelemen, Matei, Ouassima Akhrif e Azeddine Kaddouri. "Linear robust control of a synchronous motor, experimental comparison with non-linear control". International Journal of Control 73, n. 7 (gennaio 2000): 624–38. http://dx.doi.org/10.1080/002071700219461.

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24

Chen, George Zheng. "Linear and non-linear pseudocapacitances with or without diffusion control". Progress in Natural Science: Materials International 31, n. 6 (dicembre 2021): 792–800. http://dx.doi.org/10.1016/j.pnsc.2021.10.011.

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25

Turner, M. C., I. Postlethwaite e D. J. Walker. "Non-linear tracking control for multivariable constrained input linear systems". International Journal of Control 73, n. 12 (gennaio 2000): 1160–72. http://dx.doi.org/10.1080/002071700414248.

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26

McGuire, Justin, Murray T. Batchelor e Brian Davies. "Linear and optimal non-linear control of one-dimensional maps". Physics Letters A 233, n. 4-6 (settembre 1997): 361–64. http://dx.doi.org/10.1016/s0375-9601(97)00486-6.

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27

Al-Khazraji, Ayman, e Karim M. Aljebory. "Robust Adaptive Type-2 Fuzzy Sliding Mode Control for Non-Linear uncertain SISO systems". Journal of Control Engineering and Technology 4, n. 4 (31 ottobre 2014): 243–56. http://dx.doi.org/10.14511/jcet.2014.040401.

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28

Schöley, Alexander, Magdalena Gierschner e Torsten Jeinsch. "Non-linear Control of Grid-Side Inverters". IFAC-PapersOnLine 53, n. 2 (2020): 13384–89. http://dx.doi.org/10.1016/j.ifacol.2020.12.175.

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29

Rantzer, Anders, e Sven Hedlund. "Density and Cost in Non-linear Control". European Journal of Control 9, n. 2-3 (gennaio 2003): 285–95. http://dx.doi.org/10.3166/ejc.9.285-295.

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30

ŻAKJ, STANISLAW H., e CARL A. MACCARLEY. "State-feedback control of non-linear systems†". International Journal of Control 43, n. 5 (maggio 1986): 1497–514. http://dx.doi.org/10.1080/00207178608933554.

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31

ROTELLA, F., e G. DAUPHIN-TANGUY. "Non-linear systems: identification and optimal control". International Journal of Control 48, n. 2 (agosto 1988): 525–44. http://dx.doi.org/10.1080/00207178808906195.

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32

FAIBUSOVICH, L. E. "Explicitly solvable non-linear optimal control problems". International Journal of Control 48, n. 6 (dicembre 1988): 2507–26. http://dx.doi.org/10.1080/00207178808906344.

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33

Lyshevski, S. E. "Non-linear multivariable flight control of aircraft". Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 212, n. 2 (1 febbraio 1998): 137–44. http://dx.doi.org/10.1243/0954410981532207.

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Innovative design methods are needed for advanced aircraft in response to requirements towards substantial performance improvements. Functionally and operationally, the aircraft must be considered as the highly coupled non-linear multi-input multi-output system, i.e. the aerodynamics have to be mapped by non-linear differential or difference equations. To improve flying and handling qualities, to increase manœuvrability and to expand the operating envelope, an innovative optimization procedure is developed to design the constrained controllers for multi-input multi-output aircraft. In particular, a bounded control law is synthesized by employing the Hamilton-Jacobi theory, and the admissibility concept is used to study the stability of the resulting closed-loop system. The developed optimization procedure is applied to a non-linear ninth-order model of an AFTI/F-16 aircraft. A bounded controller is designed, and modelling results are presented to demonstrate the dynamic performance of the resulting closed-loop system.

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34

Hu, X. B., e W. H. Chen. "Model predictive control for non-linear missiles". Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, n. 8 (30 novembre 2007): 1077–89. http://dx.doi.org/10.1243/09596518jsce394.

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35

Fantoni, I., R. Lozano e SC Sinha. "Non-linear Control for Underactuated Mechanical Systems". Applied Mechanics Reviews 55, n. 4 (2002): B67. http://dx.doi.org/10.1115/1.1483350.

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36

Shahruz, S. M. "Boundary control of Kirchhoff's non-linear string". International Journal of Control 72, n. 6 (gennaio 1999): 560–63. http://dx.doi.org/10.1080/002071799220993.

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37

Su, Juhng-Perng, e Chun-Chieh Wang. "Complementary sliding control of non-linear systems". International Journal of Control 75, n. 5 (gennaio 2002): 360–68. http://dx.doi.org/10.1080/00207170110112250.

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38

Cannon, M., B. Kouvaritakis, Y. I. Lee e A. C. Brooms. "Efficient non-linear model based predictive control". International Journal of Control 74, n. 4 (gennaio 2001): 361–72. http://dx.doi.org/10.1080/00207170010010597.

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39

Benosman, Mouhacine, e Gökhan M. Atınç. "Non-linear adaptive control for electromagnetic actuators". IET Control Theory & Applications 9, n. 2 (19 gennaio 2015): 258–69. http://dx.doi.org/10.1049/iet-cta.2013.1011.

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40

Liang, Yew-Wen, e Der-Cherng Liaw. "Robust control of non-linear affine systems". Applied Mathematics and Computation 137, n. 2-3 (maggio 2003): 337–47. http://dx.doi.org/10.1016/s0096-3003(02)00129-7.

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41

Alvarez, José, Jesús Alvarez e C. Martínez. "Non-Linear Analysis of Distillation Control Structures". IFAC Proceedings Volumes 25, n. 5 (aprile 1992): 225–30. http://dx.doi.org/10.1016/s1474-6670(17)50996-9.

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42

Menini, Laura. "Non-linear control for underactuated mechanical systems". Automatica 38, n. 11 (novembre 2002): 2030–31. http://dx.doi.org/10.1016/s0005-1098(02)00102-4.

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43

Agrawal, A. K., J. N. Yang e J. C. Wu. "Non-linear control strategies for Duffing systems". International Journal of Non-Linear Mechanics 33, n. 5 (settembre 1998): 829–41. http://dx.doi.org/10.1016/s0020-7462(97)00055-3.

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44

Bourdache-Siguerdidjane, Houria, e Michel Fliess. "Optimal feedback control of non-linear systems". Automatica 23, n. 3 (maggio 1987): 365–72. http://dx.doi.org/10.1016/0005-1098(87)90009-4.

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45

Hall, S. G., e M. J. Stephenso. "Optimal Control of Stochastic Non-linear Models". IFAC Proceedings Volumes 22, n. 5 (giugno 1989): 123–30. http://dx.doi.org/10.1016/s1474-6670(17)53441-2.

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46

Burger, J., e J. Taoud. "Optimal Control of a Non-Linear System". IFAC Proceedings Volumes 22, n. 4 (giugno 1989): 141–45. http://dx.doi.org/10.1016/s1474-6670(17)53533-8.

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47

., Anamika Vyas Ojha. "CONTROL OF NON-LINEAR SYSTEM USING BACKSTEPPING". International Journal of Research in Engineering and Technology 04, n. 05 (25 maggio 2015): 606–10. http://dx.doi.org/10.15623/ijret.2015.0405111.

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48

SIRA-RAMIREZ, HEBERTT. "Variable structure control of non-linear systems". International Journal of Systems Science 18, n. 9 (gennaio 1987): 1673–89. http://dx.doi.org/10.1080/00207728708967144.

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49

KRIKELIS, N. J., e K. I. KIRIAKIDIS. "Optimal feedback control of non-linear systems". International Journal of Systems Science 23, n. 12 (dicembre 1992): 2141–53. http://dx.doi.org/10.1080/00207729208949445.

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50

SINHA, A. S. C., e H. O. YURTSEVEN. "Non-linear control algorithm for robot manipulators". International Journal of Systems Science 25, n. 2 (febbraio 1994): 225–36. http://dx.doi.org/10.1080/00207729408928956.

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