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Journal articles on the topic 'Dynamic gain'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Doyle III, Francis J., Harpreet S. Kwatra, and James S. Schwaber. "Dynamic gain scheduled process control." Chemical Engineering Science 53, no. 15 (August 1998): 2675–90. http://dx.doi.org/10.1016/s0009-2509(98)00089-x.

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2

Jones, C. D. C., M. H. Lowenberg, and T. S. Richardson. "Tailored Dynamic Gain-Scheduled Control." Journal of Guidance, Control, and Dynamics 29, no. 6 (November 2006): 1271–81. http://dx.doi.org/10.2514/1.17295.

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3

Jones, C. D. C., T. S. Richardson, and M. H. Lowenberg. "Dynamic gain-scheduled control of the ICE 101-TV." Aeronautical Journal 109, no. 1102 (December 2005): 593–607. http://dx.doi.org/10.1017/s0001924000000932.

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Abstract This paper shows the theoretical development and application of dynamic gain scheduled control – a novel method for the control of nonlinear systems – to an aircraft model. The idea behind this method is to schedule the control law gains with a fast varying state variable rather than with a slow varying state or an input parameter. This is advantageous as it is then possible to schedule the gains with a state variable that is dominant in the mode that we are most interested in controlling. The use of this type of gain scheduling is shown to improve the transient response of the aircraft model when stepping between trim conditions and to overcome some of the problems associated with conventional gain scheduled controllers (such as control surface position limit saturation). ‘Hidden coupling terms’ that introduce unwanted dynamics when scheduling gains with a fast state (rather than the input design parameter) are eliminated directly by applying a transformation to the classical parameter-scheduled gain distributions which are calculated using eigenstructure assignment. A second order longitudinal model and a 5th order longitudinal/lateral model of the ICE 101-TV tailless delta-wing aircraft configuration are used to demonstrate the design process.

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4

Azami, N. "All-fiber dynamic gain slope compensator." Optics Communications 230, no. 4-6 (February 2004): 325–29. http://dx.doi.org/10.1016/j.optcom.2003.11.060.

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5

Sripati, Arun P., and Kenneth O. Johnson. "Dynamic Gain Changes During Attentional Modulation." Neural Computation 18, no. 8 (August 2006): 1847–67. http://dx.doi.org/10.1162/neco.2006.18.8.1847.

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Attention causes a multiplicative effect on firing rates of cortical neurons without affecting their selectivity (Motter, 1993; McAdams & Maunsell, 1999a) or the relationship between the spike count mean and variance (McAdams & Maunsell, 1999b). We analyzed attentional modulation of the firing rates of 144 neurons in the secondary somatosensory cortex (SII) of two monkeys trained to switch their attention between a tactile pattern recognition task and a visual task. We found that neurons in SII cortex also undergo a predominantly multiplicative modulation in firing rates without affecting the ratio of variance to mean firing rate (i.e., the Fano factor). Furthermore, both additive and multiplicative components of attentional modulation varied dynamically during the stimulus presentation. We then used a standard conductance-based integrate-and-fire model neuron to ascertain which mechanisms might account for a multiplicative increase in firing rate without affecting the Fano factor. Six mechanisms were identified as biophysically plausible ways that attention could modify the firing rate: spike threshold, firing rate adaptation, excitatory input synchrony, synchrony between all inputs, membrane leak resistance, and reset potential. Of these, only a change in spike threshold or in firing rate adaptation affected model firing rates in a manner compatible with the observed neural data. The results indicate that only a limited number of biophysical mechanisms can account for observed attentional modulation.

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6

Richardson, T., M. Lowenberg, C. Jones, and A. Dubs. "Dynamic gain scheduled control of a Hawk scale model." Aeronautical Journal 111, no. 1121 (July 2007): 461–69. http://dx.doi.org/10.1017/s0001924000004723.

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Abstract When designing flight control laws using linearisations of an aircraft model about different flight conditions, some form of scheduling of the resultant gains will often be required to implement the controller over wide operating regions. In practice, the controller gains are often scheduled against relatively slowly-varying system states such as altitude or velocity. However, it may also be desirable to schedule gains against rapidly-varying states such as angle-of-attack, thereby generating a cyclic dependence through hidden coupling terms. Previous published work at Bristol has developed a numerical method of accounting for this dependence when scheduling state feedback gains against coupled states. The resulting ‘dynamic gain schedule’ is shown to significantly improve the transient response of the aircraft model during rapid manoeuvring and to reduce the chances of control surface actuator position limit saturation. In this paper, the novel design process, using eigenstructure assignment, is applied to a mathematical second-order longitudinal aircraft model which represents an approximate BAe Hawk wind-tunnel model. The dynamic gain scheduled controller is shown to work extremely well in practice when applied to the closed-loop experimental rig. Despite the highly nonlinear characteristics of the model aerodynamics and tailplane actuation system, as well as unmodelled high turbulence levels, dynamic gain scheduling demonstrates stable closed loop control even in regions where the nonlinearities are such that conventional gain scheduling fails to produce a stable response.

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7

Handogo, Renanto, Avon T. H., and Joko Lelono. "Comparison of Steady State and Dynamic Interaction Measurements in Multiloop Control Systems." ASEAN Journal of Chemical Engineering 5, no. 1 (June 1, 2005): 1. http://dx.doi.org/10.22146/ajche.50158.

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The applicability of the steady-state Relative Gain Array (RGA) to measure dynamic process interactions in a multiloop control system was investigated. Several transfer function matrices were chosen, and the gains, time constants, and dead times of their elements were varied to represent the systems with dominant dynamic interactions. It was shown that the steady-state RGA method predicted the controller pairing accurately if the pairing elements recommended by RGA had the bigger gains and the same or smaller time constants compared to other elements in the corresponding rows. When these conditions were not met, the RGA would give a wrong result, and dynamic interaction measurements, such as the Average Dynamic Gain Array (ADGA) and the Inverse Nyquist Array (lNA), should be used instead to determine the best controller pairing in a multiloop control system. Keywords: Control pairing, dynamic process interaction, multiloop control systems, Relative Gain Array (RGA), and steady state.

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8

Kroeger, Brian W., and John J. Kurtz. "Speech enhancement system having dynamic gain control." Journal of the Acoustical Society of America 86, no. 6 (December 1989): 2477. http://dx.doi.org/10.1121/1.398765.

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9

Wang, Michael Mao. "Dynamic Gain Management for On-Channel Repeaters." IEEE Transactions on Broadcasting 59, no. 4 (December 2013): 685–92. http://dx.doi.org/10.1109/tbc.2013.2284417.

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10

Liansheng Tan, Yan Yang, Wei Zhang, and M. Zukerman. "On control gain selection in dynamic-RED." IEEE Communications Letters 9, no. 1 (January 2005): 81–83. http://dx.doi.org/10.1109/lcomm.2005.1375249.

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11

Hannewald, K., S. Glutsch, and F. Bechstedt. "Dynamic theory of excitonic hyper-Raman gain." Physical Review B 58, no. 23 (December 15, 1998): 15336–39. http://dx.doi.org/10.1103/physrevb.58.15336.

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12

Zhang, Shi Hai, Zi Miao Zhang, and Zhong Min Wang. "Research on Gain Scheduling Control of Online Dynamic Balance for Flexible Spindle." Applied Mechanics and Materials 488-489 (January 2014): 1083–86. http://dx.doi.org/10.4028/www.scientific.net/amm.488-489.1083.

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The gain scheduling control is widely used in the system, whose dynamics parameters change with the operating conditions. In order to adapt the dynamic parameter variety of the high-speed and flexible spindle, the gain scheduling control method is applied to control the unbalance vectors of the correcting faces. The algorithm principle of gain scheduling control and its control method are given in the paper. The flexible spindle experiment system is designed to test the dynamic balance effect of gain scheduling control method. The experiment result indicates that the unbalance vectors of the correcting faces tend to be stable and close to the actual demand based on the gain scheduling control, and the residual unbalance vibration of the two monitoring points tend to be average and small.

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13

WIESENFELD, J. M. "GAIN DYNAMICS AND ASSOCIATED NONLINEARITIES IN SEMICONDUCTOR OPTICAL AMPLIFIERS." International Journal of High Speed Electronics and Systems 07, no. 01 (March 1996): 179–222. http://dx.doi.org/10.1142/s0129156496000086.

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A rich variety of dynamical processes underlie the operation of active semiconductor light-emitting devices, such as semiconductor optical amplifiers. These processes include interband and intraband carrier dynamics. Interband processes comprise spontaneous recombination, both radiative and Auger, stimulated radiative recombination, and carrier transport. Intraband processes comprise carrier heating and cooling and spectral hole-burning, among others. The dynamical processes affect both the gain and refractive index of the semiconductor optical amplifier. In this article, these dynamic processes and their physical origins are reviewed. Under conditions of large, time-varying changes in carrier density or intraband carrier distribution, nonlinear gain and refraction becomes significant. For applications requiring linear amplification, such nonlinearities are deleterious. However, for many applications such nonlinearities can be the basis for useful device functions. In particular, the nonlinearities of cross-gain modulation, cross-phase modulation, and four-wave mixing in semiconductor optical amplifiers have been applied for the functions of wavelength conversion, optical time-demultiplexing, clock recovery, and trans-multiplexing. Such nonlinear devices based on semiconductor optical amplifiers and their effects on propagating optical signals are also reviewed.

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14

ÖZER, E., J. H. WELLS, K. W. McMILLIN, C. P. HO, and N. Y. HUANG. "DYNAMIC GAIN MATRIX APPROACH TO MODELING OF DYNAMIC MODIFIED ATMOSPHERE PACKAGING SYSTEMS." Journal of Food Process Engineering 20, no. 3 (July 1997): 231–48. http://dx.doi.org/10.1111/j.1745-4530.1997.tb00420.x.

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15

ZHAO SHANG-HONG and XIANG DE-QUAN. "DYNAMIC GAIN IN THE AMPLIFICATION OF OPTICAL SOLITON." Acta Physica Sinica 43, no. 10 (1994): 1615. http://dx.doi.org/10.7498/aps.43.1615.

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16

Peralez, Johan, Vincent Andrieu, Madiha Nadri, and Ulysse Serres. "Self-triggered control via dynamic high-gain scaling." IFAC-PapersOnLine 49, no. 18 (2016): 356–61. http://dx.doi.org/10.1016/j.ifacol.2016.10.191.

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17

Zhang, Minglei, Qiyuan Liu, and Xiaohua Fan. "Gain‐boosted dynamic amplifier for pipelined‐SAR ADCs." Electronics Letters 53, no. 11 (May 2017): 708–9. http://dx.doi.org/10.1049/el.2017.0146.

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18

Giles, C. R., and D. J. Di Giovanni. "Dynamic gain equalization in two-stage fiber amplifiers." IEEE Photonics Technology Letters 2, no. 12 (December 1990): 866–68. http://dx.doi.org/10.1109/68.62012.

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19

Thomas, Diana M., Jesus E. Navarro-Barrientos, Daniel E. Rivera, Steven B. Heymsfield, Carl Bredlau, Leanne M. Redman, Corby K. Martin, Sally A. Lederman, Linda M Collins, and Nancy F. Butte. "Dynamic energy-balance model predicting gestational weight gain." American Journal of Clinical Nutrition 95, no. 1 (December 14, 2011): 115–22. http://dx.doi.org/10.3945/ajcn.111.024307.

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20

Köse, İ. Emre, and Carsten W. Scherer. "GAIN-SCHEDULED CONTROL USING DYNAMIC INTEGRAL QUADRATIC CONSTRAINTS." IFAC Proceedings Volumes 39, no. 9 (2006): 220–25. http://dx.doi.org/10.3182/20060705-3-fr-2907.00039.

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21

Neth, C. T., J. L. Souman, D. Engel, U. Kloos, H. H. Bulthoff, and B. J. Mohler. "Velocity-Dependent Dynamic Curvature Gain for Redirected Walking." IEEE Transactions on Visualization and Computer Graphics 18, no. 7 (July 2012): 1041–52. http://dx.doi.org/10.1109/tvcg.2011.275.

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22

Scherer, Carsten W., and I. Emre Kose. "Gain-Scheduled Control Synthesis Using Dynamic $D$-Scales." IEEE Transactions on Automatic Control 57, no. 9 (September 2012): 2219–34. http://dx.doi.org/10.1109/tac.2012.2184609.

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23

Seong-Jun Oh and K. M. Wasserman. "Dynamic spreading gain control in multiservice CDMA networks." IEEE Journal on Selected Areas in Communications 17, no. 5 (May 1999): 918–27. http://dx.doi.org/10.1109/49.768205.

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24

Zhang, L. M., J. E. Carroll, and C. Tsang. "Dynamic response of the gain-coupled DFB laser." IEEE Journal of Quantum Electronics 29, no. 6 (June 1993): 1722–27. http://dx.doi.org/10.1109/3.234427.

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25

Ludvigsen, Carl. "Dynamic automatic gain control in a hearing aid." Journal of the Acoustical Society of America 115, no. 4 (2004): 1399. http://dx.doi.org/10.1121/1.1738258.

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26

Liu, Lin, Craig Michie, Anthony E. Kelly, and Ivan Andonovic. "The Dynamic Gain Modulation Performance of Adjustable Gain-Clamped Semiconductor Optical Amplifiers (AGC-SOA)." Journal of Lightwave Technology 29, no. 22 (November 2011): 3483–89. http://dx.doi.org/10.1109/jlt.2011.2171669.

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27

Tadic, Niksa, Alija Dervic, Milena Erceg, Bernhard Goll, and Horst Zimmermann. "A 54.2-dB Current Gain Dynamic Range, 1.78-GHz Gain-Bandwidth Product CMOS VCCA2." IEEE Transactions on Circuits and Systems II: Express Briefs 66, no. 1 (January 2019): 46–50. http://dx.doi.org/10.1109/tcsii.2018.2837152.

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28

Wang, Liang, and Chester Shu. "Dynamic Control of Gain Profile in Fiber-Optical Parametric Amplifier by Gain-Transparent SBS." IEEE Photonics Technology Letters 25, no. 20 (October 2013): 1996–99. http://dx.doi.org/10.1109/lpt.2013.2280695.

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29

Thomas, D. H., and J. P. von der Weid. "Dynamic gain fluctuations in all-optic ring-laser gain-clamped erbium-doped fiber amplifiers." Microwave and Optical Technology Letters 44, no. 1 (2004): 77–80. http://dx.doi.org/10.1002/mop.20552.

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30

Cheng, Xu, Liang Zhang, and Xianjin Deng. "High-dynamic-range programmable gain amplifier with linear-in-dB and DAC gain control." Analog Integrated Circuits and Signal Processing 94, no. 1 (November 24, 2017): 83–96. http://dx.doi.org/10.1007/s10470-017-1086-0.

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31

Seo Yeon Park, Hyang Kyun Kim, Gap Yeol Lyu, Sun Mo Kang, and Sang-Yung Shin. "Dynamic gain and output power control in a gain-flattened erbium-doped fiber amplifier." IEEE Photonics Technology Letters 10, no. 6 (June 1998): 787–89. http://dx.doi.org/10.1109/68.681484.

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32

di Muro, R. "The Er/sup 3+/-fiber gain coefficient derived from a dynamic gain tilt technique." Journal of Lightwave Technology 18, no. 3 (March 2000): 343–47. http://dx.doi.org/10.1109/50.827506.

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33

Harel, M., G. Agranovich, and M. Brand. "Optimal periodic gain scheduling for bipedal walking with hybrid dynamics." Robotica 34, no. 8 (December 9, 2014): 1811–21. http://dx.doi.org/10.1017/s0263574714002586.

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SUMMARYWe present an optimal gain scheduling control design for bipedal walking with minimum tracking error. We obtained a linear approximation by linearizing the nonlinear hybrid dynamic model about a nominal periodic trajectory. This linearization allows us to identify the linear model as a linear periodic system. An optimal feedback was designed using Bellman's dynamic programming. The linear periodic system allows us to determine a linear quadratic regulator (LQR) for a single period and to set the Hamilton-Jacobi-Bellman (HJB) function in a linear quadratic form. In this way, the dynamic programming yielded an admissible continuous gain scheduling that was designed with regard to the hybrid dynamics of the system. We tuned the optimization parameters such that the tracking error and the average energy consumption are minimized. Due to linearization, we were able to examine the stability of the approximated periodic system achieved by the periodic gain according to Floquet's theory, by calculating the monodromy matrix of the closed-loop hybrid system. In addition to determining stability, the eigenvalues of this approximated monodromy matrix allowed us to evaluate the settling time of the system. This approach presents a direct method for optimal solution of locomotion control according to a given reference trajectory.

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34

Wu, Guanghui, Xiuyun Meng, and Jie Wang. "Robust Adaptive Nonlinear Dynamic Inversion Control for an Air-breathing Hypersonic Vehicle." MATEC Web of Conferences 220 (2018): 08001. http://dx.doi.org/10.1051/matecconf/201822008001.

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This paper presents a robust adaptive nonlinear dynamic inversion control approach for the longitudinal dynamics of an air-breathing hypersonic vehicle. The proposed approach adopts a fast adaptation law using high-gain learning rate, while a low-pass filter is synthesized with the modified adaptive scheme to filter out the high-frequency content of the estimates. This modified high-gain adaptive scheme achieves a good transient process and a nice robust property with respect to parameter uncertainties, without exciting high-frequency oscillations. Based on input-output linearization, the nonlinear hypersonic dynamics are transformed into equivalent linear systems. Therefore, the pole placement technique is applied to design the baseline nonlinear dynamic inversion controller. Finally, the simulation results of the modified adaptive nonlinear dynamic inversion control law demonstrate the proposed control approach provides robust tracking of reference trajectories.

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35

Carrara, F., P. Filoramo, and G. Palmisano. "High-dynamic-range variable gain amplifier with temperature compensation and linear-in-decibel gain control." Electronics Letters 40, no. 6 (2004): 363. http://dx.doi.org/10.1049/el:20040247.

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36

Kim, Seyoung, Fay B. Horak, Patricia Carlson-Kuhta, and Sukyung Park. "Postural Feedback Scaling Deficits in Parkinson's Disease." Journal of Neurophysiology 102, no. 5 (November 2009): 2910–20. http://dx.doi.org/10.1152/jn.00206.2009.

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Many differences in postural responses have been associated with age and Parkinson's disease (PD), but until now there has been no quantitative model to explain these differences. We developed a feedback control model of body dynamics that could reproduce the postural responses of young subjects, elderly subjects, and subjects with PD, and we investigated whether the postural impairments of subjects with PD can be described as an abnormal scaling of postural feedback gain. Feedback gains quantify how the nervous system generates compensatory joint torques based on kinematic responses. Seven subjects in each group experienced forward postural perturbations to seven different backward support surface translations ranging from 3- to 15-cm amplitudes and with a constant duration of 275 ms. Ground reaction forces and joint kinematics were measured to obtain joint torques from inverse dynamics. A full-state feedback controller with a two-segment body dynamic model was used to simulate joint kinematics and kinetics in response to perturbations. Results showed that all three subject groups gradually scaled postural feedback gains as a function of perturbation amplitudes, and the scaling started even before the maximum allowable ankle torque was reached. This result implies that the nervous system takes body dynamics into account and adjusts postural feedback gains to accommodate biomechanical constraints. PD subjects showed significantly smaller than normal ankle feedback gain with low scaling and larger hip feedback gain, which led to an early violation of the flat-foot constraint and unusually small (bradykinetic) postural responses. Our postural feedback control model quantitatively described the postural abnormality of the patients with PD as abnormal feedback gains and reduced ability to modify postural feedback gain with changes in postural challenge.

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37

Wang, Kun Qi, Guang Qi Ren, and Zhu Feng Shao. "A New Method on Illumination Measurement to Dynamic and Static Light Sources." Advanced Materials Research 411 (November 2011): 203–7. http://dx.doi.org/10.4028/www.scientific.net/amr.411.203.

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A novel measurement system for the dynamic and static light sources is designed in this paper. The method of dynamic gain adjustment measurement is proposed according to the national standards on luminous intensity of dynamic and static light sources. The method is achieved by the way that microcontroller controlled the analog switches to switch automatically in different ranges and adjust the gain automatically depend on the light intensity. By designing the overall structure of the system and using the dynamic gain adjustment method, the range of dynamic measuring illumination can be from 0.001 up to 2000 . The illumination measurement to static light source and the dynamic light source in the peak luminous intensity are achieved via the combination of interval control and dynamic gain adjustment.

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38

Doherty, Paul F., Elizabeth A. Marschall, and Thomas C. Grubb. "Balancing conservation and economic gain: a dynamic programming approach." Ecological Economics 29, no. 3 (June 1999): 349–58. http://dx.doi.org/10.1016/s0921-8009(98)00057-3.

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39

Costa, Eduardo F., and Vilma A. Oliveira. "Gain scheduled controllers for dynamic systems using sector nonlinearities." Automatica 38, no. 7 (July 2002): 1247–50. http://dx.doi.org/10.1016/s0005-1098(02)00007-9.

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40

Mc Avoy, Thomas, Yaman Arkun, Rong Chen, Derek Robinson, and P. David Schnelle. "A new approach to defining a dynamic relative gain." Control Engineering Practice 11, no. 8 (August 2003): 907–14. http://dx.doi.org/10.1016/s0967-0661(02)00207-1.

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41

Luk, W. K., and R. H. Dennard. "A novel dynamic memory cell with internal voltage gain." IEEE Journal of Solid-State Circuits 40, no. 4 (April 2005): 884–94. http://dx.doi.org/10.1109/jssc.2004.842854.

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42

Costa, Eduardo F., and Vilma A. Oliveira. "Gain schedulled controllers for dynamic systems with sector nonlinearities." IFAC Proceedings Volumes 32, no. 2 (July 1999): 2482–87. http://dx.doi.org/10.1016/s1474-6670(17)56422-8.

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43

Mc Avoy, Thomas, Yaman Arkun, Rong Chen, Derek Robinson, and P. David Schnelle. "A new approach to defining a dynamic relative gain." IFAC Proceedings Volumes 34, no. 25 (June 2001): 415–20. http://dx.doi.org/10.1016/s1474-6670(17)33859-4.

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44

Kemme, S. A., R. K. Kostuk, and C. K. Y. Chun. "Dynamic coherence measurements of index- and gain-guided VCSELs." IEEE Photonics Technology Letters 9, no. 5 (May 1997): 554–56. http://dx.doi.org/10.1109/68.588092.

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45

Herda, Robert, Samuli Kivistö, and Oleg G. Okhotnikov. "Dynamic gain induced pulse shortening in Q-switched lasers." Optics Letters 33, no. 9 (April 30, 2008): 1011. http://dx.doi.org/10.1364/ol.33.001011.

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46

Crauel, Hans, Tobias Damm, and Achim Ilchmann. "STABILIZATION OF LINEAR SYSTEMS BY DYNAMIC HIGH-GAIN ROTATION." IFAC Proceedings Volumes 38, no. 1 (2005): 67–72. http://dx.doi.org/10.3182/20050703-6-cz-1902.00232.

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47

Barge, M., D. Battarel, and J. Ld B. de la Tocnaye. "A polymer-dispersed liquid crystal-based dynamic gain equalizer." Journal of Lightwave Technology 23, no. 8 (August 2005): 2531–41. http://dx.doi.org/10.1109/jlt.2005.850037.

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48

Perng, J. W., B. F. Wu, H. I. Chin, and T. T. Lee. "Gain-Phase Margin Analysis of Dynamic Fuzzy Control Systems." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 5 (October 2004): 2133–39. http://dx.doi.org/10.1109/tsmcb.2004.831772.

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49

Scherer, Carsten W. "Gain-Scheduling Control With Dynamic Multipliers by Convex Optimization." SIAM Journal on Control and Optimization 53, no. 3 (January 2015): 1224–49. http://dx.doi.org/10.1137/140985871.

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50

Wang, Y., E. S. Yang, and W. I. Wang. "High gain and wide dynamic range punchthrough heterojunction phototransistors." Journal of Applied Physics 74, no. 11 (December 1993): 6978–81. http://dx.doi.org/10.1063/1.355048.

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