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Journal articles on the topic 'Adaptive PID'

Author: Grafiati

Published: 23 June 2021

Last updated: 1 February 2022

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1

Taeib, Adel, and Abdelkader Chaari. "PID Controller Based Adaptive PSO." International Review of Automatic Control (IREACO) 7, no. 1 (January 31, 2014): 31. http://dx.doi.org/10.15866/ireaco.v7i1.1288.

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2

Bányász, Cs, and L. Keviczky. "A Simple Adaptive PID Tuner." IFAC Proceedings Volumes 31, no. 18 (July 1998): 471–77. http://dx.doi.org/10.1016/s1474-6670(17)42036-2.

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3

Amaral, W., J. C. Batista, G. Favier, L. Gimeno, A. Llamosas, and R. Machado. "PID Controller for Adaptive Applications." IFAC Proceedings Volumes 19, no. 13 (November 1986): 161–66. http://dx.doi.org/10.1016/s1474-6670(17)59534-8.

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4

Sun, Yan Xia, and Zeng Hui Wang. "Adaptive Optimal Digital PID Controller." Applied Mechanics and Materials 789-790 (September 2015): 1021–26. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.1021.

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It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

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5

Hägglund, T., and K. J. Åström. "An Industrial Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (April 1990): 251–56. http://dx.doi.org/10.1016/s1474-6670(17)52729-9.

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6

Thompson, S., and E. G. McCluskey. "An Expert Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (April 1990): 257–62. http://dx.doi.org/10.1016/s1474-6670(17)52730-5.

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7

Keviczky, L., and C. S. Bányász. "A Completely Adaptive PID Regulator." IFAC Proceedings Volumes 21, no. 9 (August 1988): 89–95. http://dx.doi.org/10.1016/s1474-6670(17)54708-4.

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8

Bartlomiej, B., and Ł. Andrzej. "The Adaptive Robust PID Controller." IFAC Proceedings Volumes 21, no. 9 (August 1988): 227–32. http://dx.doi.org/10.1016/s1474-6670(17)54730-8.

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9

Burakov, M. V., V. F. Shishlakov, and A. S. Konovalov. "ADAPTIVE NEURAL NETWORK PID CONTROLLER." Issues of radio electronics, no. 10 (October 20, 2018): 86–92. http://dx.doi.org/10.21778/2218-5453-2018-10-86-92.

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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coefficients at which the dynamics of the system approach the dynamics of the reference process. The calculation of weights and displacements of neurons in Hopfield networks used for identification and control is based on the construction of Lyapunov functions. The proposed methodology can be used to organize adaptive control of a wide class of linear dynamic systems with variable parameters. The results of the simulation in the article show the effectiveness of the proposed method.

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10

Benaskeur, A. R., and A. Desbiens. "Backstepping-based adaptive PID control." IEE Proceedings - Control Theory and Applications 149, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1049/ip-cta:20020100.

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11

Zhang, Yan Hong, De An Zhao, and Jian Sheng Zhang. "Research on Single Neuron Adaptive PID Control." Applied Mechanics and Materials 150 (January 2012): 174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.150.174.

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As a branch of the intelligent control, neural networks is applied in control more and more widely, the single neuron adaptive PID control algorithm is studied in this paper, and the program is written by MATLAB, the common object of single neuron adaptive PID is simulated, and the effect of single neuron adaptive PID control parameters on control effect is analyzed, experimental results show that the single neuron PID control has more obvious advantages than general PID control.

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12

Joshi, Avinash S., and R. B. Ghatikar. "Self Tuning and Adaptive PID Controller." IETE Journal of Education 30, no. 2 (April 1989): 63–68. http://dx.doi.org/10.1080/09747338.1989.11436235.

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13

Badr, Ahmed Z. "Neural Network Based Adaptive PID Controller." IFAC Proceedings Volumes 30, no. 6 (May 1997): 251–57. http://dx.doi.org/10.1016/s1474-6670(17)43373-8.

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14

Wang, Ya Gang, and Zhen Ping Tian. "Adaptive PID controllers with robustness specifications." International Journal of Modelling, Identification and Control 20, no. 2 (2013): 148. http://dx.doi.org/10.1504/ijmic.2013.056187.

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15

Tsai, Ching-Chih, Chien-Cheng Yu, and Chia-Ta Tsai. "Adaptive ORFWNN-Based Predictive PID Control." International Journal of Fuzzy Systems 21, no. 5 (May 16, 2019): 1544–59. http://dx.doi.org/10.1007/s40815-019-00650-w.

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16

de Larminat, Ph. "Adaptive PID Regulators. Ambitions and Limitations." IFAC Proceedings Volumes 21, no. 10 (August 1988): 105–10. http://dx.doi.org/10.1016/b978-0-08-036620-3.50023-5.

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17

Esfandyari, Morteza, Mohammad Ali Fanaei, and Hadi Zohreie. "Adaptive fuzzy tuning of PID controllers." Neural Computing and Applications 23, S1 (November 1, 2012): 19–28. http://dx.doi.org/10.1007/s00521-012-1215-8.

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18

He, Xiao Song. "Adaptive Fuzzy PID Control for Cutter." Applied Mechanics and Materials 310 (February 2013): 506–9. http://dx.doi.org/10.4028/www.scientific.net/amm.310.506.

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It is very difficult to accurately control the speed of paper roller and sword roller of the paper cutting machine, and the proportions of two paces control have greater randomness. Alterable fuzzy-PID (proportion integral derivative) control arithmetic is put forward based on PLC. The alterable fuzzy controller and the PI adjustor are realized by PLC (Programmable Logic Controller). The satisfied results are achieved with PID control and adaptive fuzzy control. The simulation results show that the two schemes are satisfying. And the two schemes have their own characteristics.

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19

Silveira, Antonio S., Antonio A. R. Coelho, and Francisco J. Gomes. "Model-Free Adaptive PID Controllers Applied to the Benchmark PID'12." IFAC Proceedings Volumes 45, no. 3 (2012): 370–75. http://dx.doi.org/10.3182/20120328-3-it-3014.00063.

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20

Liu, Bin, Gang Yao, Xiao Bing Xiao, and Xu Sheng Yin. "The Research on Self-Adaptive Fuzzy PID Controller." Applied Mechanics and Materials 373-375 (August 2013): 1462–65. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1462.

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The parameters of the traditional proportional-integral-derivative (PID) controller are hard to automatically adjust when the controlled object changes, which controls ineffective for time-varying, nonlinear system. Combine the fuzzy control and PID control, and use self-adaptive fuzzy control to achieve self-tuning PID parameters online. Using matlab simulation system, the results show that the self-adaptive fuzzy PID control effects have been improved than the conventional PID control.

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21

Zhang, Qiang, Zhongyu Ding, and Meijuan Zhang. "Adaptive Self-Regulation PID Control of Course-Keeping for Ships." Polish Maritime Research 27, no. 1 (March 1, 2020): 39–45. http://dx.doi.org/10.2478/pomr-2020-0004.

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AbstractTo solve the nonlinear control problems of the unknown time-varying environmental disturbances and parametric uncertainties for ship course-keeping control, this paper presents an adaptive self-regulation PID (APID) scheme which can ensure the boundedness of all signals in the ship course-keeping control system by using the Lyapunov direct method. Compared with the traditional PID control scheme, the APID control scheme not only is independent of the model parameters and the unknown input, but also can regulate the gain of PID adaptively and resist time-varying disturbances well. Simulation results illustrate the effectiveness and the robustness of the proposed control scheme.

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22

Zuo, Xin, Jian-wei Liu, Xin Wang, and Hua-qing Liang. "Adaptive PID and Model Reference Adaptive Control Switch Controller for Nonlinear Hydraulic Actuator." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6970146.

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Nonlinear systems are modeled as piecewise linear systems at multiple operating points, where the operating points are modeled as switches between constituent linearized systems. In this paper, adaptive piecewise linear switch controller is proposed for improving the response time and tracking performance of the hydraulic actuator control system, which is essentially piecewise linear. The controller composed of PID and Model Reference Adaptive Control (MRAC) adaptively chooses the proportion of these two components and makes the designed system have faster response time at the transient phase and better tracking performance, simultaneously. Then, their stability and tracking performance are analyzed and evaluated by the hydraulic actuator control system, the hydraulic actuator is controlled by the electrohydraulic system, and its model is built, which has piecewise linear characteristic. Then the controller results are compared between PID and MRAC and the switch controller designed in this paper is applied to the hydraulic actuator; it is obvious that adaptive switch controller has better effects both on response time and on tracking performance.

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23

Zhang, Jing Dong, Rui Tang, and Yong Qiao Wei. "Fuzzy Adaptive PID Control for Parallel Machine Tool." Applied Mechanics and Materials 273 (January 2013): 660–64. http://dx.doi.org/10.4028/www.scientific.net/amm.273.660.

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The hydraulic control system, which is important composition of parallel machine tool, is a high order, nonlinear, parameter uncertain system, which seriously affect dynamic performance of machine tool, So it is very difficult to gain good performance with traditional control. By making a single-channel control of parallel machine tool as the research object, a fuzzy adaptive PID controller is proposed based on the traditional PID control and fuzzy control theory. Using the Fuzzy PID controller, PID parameters can real-time change and the system has good adaptive ability and robustness. System dynamic simulation is made by the MATLAB. Simulation results reveal that the performance of this adaptive fuzzy PID controller is much better than the conventional PID controller, even in the external disturbance and system conditions changing, this controller has the the characteristics of fast response and good robustness.

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24

Chen, Xiu Jia, and Hong Di Qiu. "Research on Single Neuron Adaptive PID Controller." Applied Mechanics and Materials 651-653 (September 2014): 826–30. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.826.

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The paper focuses on single neuron adaptive PID controller based on unsupervised Hebb algorithm, and simulation research on the controller is carried out for a second-order pure lag process system. Simulation results show that through learning and adjusting weights of single neuron adaptive PID controller, its online self-tuning ability can make timely adjustment of PID controller parameters according to controlled object changes and external disturbances in order to ensure that the stability and robustness of the system and, ultimately, more satisfactory actual control effect is obtained. At last, the control characteristics and parameter design rules are concluded.

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25

Mao, Zheng Yu, Jing Sheng Zhang, and Lu Xu Zhang. "Research on Adaptive Fuzzy PID Dust Collection System." Advanced Materials Research 722 (July 2013): 437–40. http://dx.doi.org/10.4028/www.scientific.net/amr.722.437.

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Aimed at time lag, time variant, serious inertia, nonlinearity problems and the difficulty of PID controller parameters adjusting, the combination of PID control and fuzzy control was put forward, this paper introduces a kind of design of adaptive fuzzy PID dust collection system of constant pressure with regarding the pressure of the deviation and the rate of change of deviation as the input variables in the ventilation pipe. The three parameters of PID controller can be adjusted by establishing the fuzzy control rules and practicing the fuzzy generalize. The experiment simulation and the practical test show that the response of the system with adaptive fuzzy PID control is much faster than the tradition PID control but the overshoot is lower and quickly achieve the steady-state.

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26

Hamada, Jun, Satoshi Suzuki, Tomoyasu Ichikawa, Hironori Kurihara, and Kazuya Sumida. "Adaptive PID Control of Multi-rotor Helicopter." Journal of the Robotics Society of Japan 36, no. 7 (2018): 508–15. http://dx.doi.org/10.7210/jrsj.36.508.

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27

Qiu, Xin Yun, and Yuan Gao. "Adaptive PID Controller Based on Single Neuron." Advanced Materials Research 466-467 (February 2012): 981–85. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.981.

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An adaptive PID controller based on single neuron is proposed. The properties, control algorithm, parameters tuning, the control law and the application condition of the controller are studied in the paper. To satisfy the properties of the requirements of the control system in an electromotor group, such as a broad dynamic changing range, a fast response, a little overshoot and time-variable parameter, a new-type self-optimizing PID controller based on artificial neural networks is proposed and studied. It is verified that the controller has few adjustable parameters and excellent robust performance. The results of simulation and experiment prove that the controller is superior to the traditional PID controller.

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28

Fraga-Gonzalez, Luis Fernando, Rita Q. Fuentes-Aguilar, Alejandro Garcia-Gonzalez, and Gildardo Sanchez-Ante. "Adaptive simulated annealing for tuning PID controllers." AI Communications 30, no. 5 (August 31, 2017): 347–62. http://dx.doi.org/10.3233/aic-170741.

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29

Graebe, S. F., and G. C. Goodwin. "Adaptive PID Design Exploiting Partial Prior Information." IFAC Proceedings Volumes 25, no. 14 (July 1992): 41–46. http://dx.doi.org/10.1016/s1474-6670(17)50710-7.

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30

Hang, Chang Chieh, Shi-Zhong He, and Tong Heng Lee. "The Normal-Mode-Inaction Adaptive PID Controller." IFAC Proceedings Volumes 25, no. 14 (July 1992): 383–88. http://dx.doi.org/10.1016/s1474-6670(17)50764-8.

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31

Nuella, Imma, Cheng Cheng, and Min-Sen Chiu. "Adaptive PID Controller Design for Nonlinear Systems." Industrial & Engineering Chemistry Research 48, no. 10 (May 20, 2009): 4877–83. http://dx.doi.org/10.1021/ie801227d.

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32

Macháček, Jiří, and Vladimír Bobál. "Adaptive PID Controller with On-Line Identification." IFAC Proceedings Volumes 33, no. 4 (April 2000): 437–42. http://dx.doi.org/10.1016/s1474-6670(17)38282-4.

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33

Wang, Wei, Lihong Xu, and Haigen Hu. "Neuron adaptive PID control for greenhouse environment." Journal of Industrial and Production Engineering 32, no. 5 (May 29, 2015): 291–97. http://dx.doi.org/10.1080/21681015.2015.1048752.

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34

Segovia, Juan Pablo, Daniel Sbarbaro, and Eric Ceballos. "An adaptive pattern based nonlinear PID controller." ISA Transactions 43, no. 2 (April 2004): 271–81. http://dx.doi.org/10.1016/s0019-0578(07)60036-6.

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35

Chiou, Juing-Shian, Shun-Hung Tsai, and Ming-Tang Liu. "A PSO-based adaptive fuzzy PID-controllers." Simulation Modelling Practice and Theory 26 (August 2012): 49–59. http://dx.doi.org/10.1016/j.simpat.2012.04.001.

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36

Świder, Zbigniew, and Leszek Trybus. "Plant Tests of a PID Adaptive Controller." IFAC Proceedings Volumes 33, no. 1 (February 2000): 11–16. http://dx.doi.org/10.1016/s1474-6670(17)35578-7.

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37

Nabati, Ehsan Gholamzadeh, and Sebastian Engell. "Online Adaptive Robust Tuning of PID Parameters." IFAC Proceedings Volumes 45, no. 3 (2012): 625–30. http://dx.doi.org/10.3182/20120328-3-it-3014.00106.

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38

Xie, Yuhao, and Zihan Su. "Fuzzy Adaptive PID Control for Missile Guidance." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012054. http://dx.doi.org/10.1088/1757-899x/466/1/012054.

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39

Maia, Antônio A. T., Juan C. Horta-Gutierrez, Ricardo N. N. Koury, and Luiz Machado. "Superheating control using an adaptive PID controller." HVAC&R Research 20, no. 4 (May 9, 2014): 424–34. http://dx.doi.org/10.1080/10789669.2013.874842.

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40

Huang, H. P., J. C. Jeng, and M. L. Roan. "On-line adaptive tuning for PID controllers." IEE Proceedings - Control Theory and Applications 149, no. 1 (January 1, 2002): 60–67. http://dx.doi.org/10.1049/ip-cta:20020099.

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41

Yang, Xin, Yan Li, Yasuki Kansha, and Min-Sen Chiu. "Enhanced VRFT design of adaptive PID controller." Chemical Engineering Science 76 (July 2012): 66–72. http://dx.doi.org/10.1016/j.ces.2012.04.007.

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42

Xia, Chang Gao, and Chong Cao. "Tuning of PID Parameters and Fuzzy Adaptive PID Control of the Hydrostatic Driving System." Advanced Materials Research 403-408 (November 2011): 5112–16. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5112.

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Composed of a variable displacement pump and a constant displacement motor, the hydrostatic driving system is a kind of closed speed control system with adjustable displacement. It is widely used in the field of engineering vehicle and other fields. Based on an analysis of the constitution and mathematical model of the hydrostatic driving system, the present study tuned PID parameters by using the critical proportioning method and the optimization method of NCD respectively. Then a kind of fuzzy adaptive PID controller was designed on the basis of the traditional PID control and the fuzzy control theory. In the controller, fuzzy logic was used to realize online self-tuning of PID parameters according to the motor speed error and its derivative, so that the system could have better adaptive ability and strong disturbance resisting performance. The dynamic simulation was made in MATLAB/SIMULINK. The simulation results show that the optimization method of NCD has better tuning effect and the response performance of the fuzzy adaptive PID controller is better than that of the classic one. Besides, it should be noted that a drawback was found about the fuzzy adaptive PID control. On the basis of fixed scale factors, a group of quantification factors is appropriate for a specific input signal, but for other signals, the response of the system is not so ideal. A method of adjusting quantification factors according to input signal was adopted to solve the above problem. Automatic adjusting of quantification factors was realized, and this could ensure ideal response to all input signals.

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43

Wei, Zhi Qiang, and Dan Jin. "Position Tracking System of Filling Machine Based on Compound Control Strategy." Applied Mechanics and Materials 380-384 (August 2013): 321–24. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.321.

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In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness.

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44

Cheng, Zheng Mei, Xiao Ding Guo, Xiao Ke Chen, and Jing Xin Deng. "The Simulation of Adaptive Fuzzy PID Control System on the New Test Power Source System of Small Locomotive." Applied Mechanics and Materials 103 (September 2011): 240–45. http://dx.doi.org/10.4028/www.scientific.net/amm.103.240.

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The voltage stability of test power source system about small locomotive is poor, and the response speed is slow. The theory of adaptive fuzzy PID control used in the small locomotive test power source system was proposed. The design of adaptive fuzzy PID controller and the effect of simulation were detail described. The control effect of adaptive fuzzy PID control and the traditional PID control is compared. The application value of adaptive fuzzy PID control on small locomotive test power source system has been proved.

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45

Chang, Kai, Xiao Jian Han, and Yong Yang. "Self-Adaptive PID Control of Hydraulic Quadruped Robot." Applied Mechanics and Materials 496-500 (January 2014): 1407–12. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1407.

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Servo hydraulic system is important one of driving patterns of quadruped robot. While servo hydraulic system has characteristics of parameters time-varying and strong nonlinearity, so traditional PID control pattern cannot meet all the control demands well. To solve these problems, a set of control hardware is designed for a quadruped robot and a new control algorithm which can adjust PID parameters in real-time condition based on fuzzy control theory is proposed. By deducing transfer function, establishing system model and using MATLAB simulation tools to simulate the two algorithms, we compared the two algorithms and the simulation result turns out that compared with traditional PID control algorithm, the fuzzy PID control algorithm has features such as low overshoot, high stability precision, strong robustness. At the same time, the result turns out that the fuzzy PID control algorithm is much fitter for the joint control of quadruped robot.

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46

Zhou, Jun. "Intelligent Information Control for Air System." Applied Mechanics and Materials 310 (February 2013): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.310.502.

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PID control has been widely applied in the industrial process control because of its robust and easy realization, but it is difficult to tune the parameters of PID controller, which often leads to oscillation and overshoot. Due to no repetition and random of adaptive fuzzy PID control, the authors propose a method to search for normalization PID controller parameters based on adaptive fuzzy PID control, which can be expected to have higher ability of searching for global optimal PID parameters according to the performance index of control system . The MATLAB simulation of the Adaptive fuzzy PID controller and a PID controller were carried out on the Air Conditioning System temperature. Results showed that: Response time of Adaptive fuzzy PID was 0.46 s., and maximum overshoot didn’t exceed 3.29%.The stability accuracy and rapidity of the system were able to satisfy the Air Conditioning System technical requirements.

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47

Irmawan, Erwhin, and Erwan Eko Prasetiyo. "Kendali Adaptif Neuro Fuzzy PID untuk Kestabilan Terbang Fixed Wing UAV (Adaptive Control of Neuro Fuzzy PID for Fixed Wing UAV Flight Stability)." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 1 (February 5, 2020): 73–78. http://dx.doi.org/10.22146/jnteti.v9i1.142.

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Unmanned Aerial Vehicle (UAV), especially fixed wing, are widely used to carry out various missions, namely civil and military missions. To support the implementation of this mission, it is necessary to develop an intelligent automatic control system (autopilot). In this paper, an autopilot system with adaptive neuro fuzzy PID control is developed to control lateral (pitch) and longitudinal (roll) motion, by taking advantage of PID, fuzzy, and neural network control. Therefore, robust controls which can handle non-linear conditions can be formed. This paper aims to determine the performance of adaptive control of neuro fuzzy PID controllers for longitudinal and lateral motion on UAV. The result shows that adaptive control of neuro fuzzy PID are able to control the lateral and longitudinal motion of the aircraft and able to compensate for interferences from environmental disturbances in flying condition, such as changes in direction and wind speed that causes changes in aircraft attitude. The control characteristics of neuro fuzzy PID adaptive control in lateral and longitudinal motion are relatively similar. Adaptive control of neuro fuzzy PID has better performance than fuzzy PID control, i.e., faster settling time and lower percentage of maximum overshoot.

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48

Shi, Gong She, Lei Huang, and Wei Hu. "The Brushless DC Motor Adaptive Fuzzy PID Servo Controller Design." Applied Mechanics and Materials 246-247 (December 2012): 838–41. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.838.

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The brushless DC motor (BLDCM) non-linear and the complexity of the working conditions are likely to cause the conventional PID servo control performance is not satisfactory. In order to improve the performance of the BLDCM servo control system and PID parameter tuning efficiency, this paper designs an adaptive fuzzy PID controller. Fuzzy logic PID controller parameters Kp, Ki, Kd are adjusted online real time to achieve the effect of optimal control, the BLDCM speed is as to the control object, and in the Matlab of Simulink toolbox simulation is used to achieve speed closed loop of BLDCM. According to comparative analysis of the conventional PID and adaptive fuzzy PID of Dynamic response curve, adaptive Fuzzy PID quick start for brushless DC motors, anti-disturbance has better control effect.

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49

Chen, Jie, Wen Jun Xu, Ben Wang, Yu Gang Duan, and Xiao Hui Zhang. "Fuzzy-Adaptive PID Based Tow Tension Controller for Robotic Automated Fiber Placement." Applied Mechanics and Materials 643 (September 2014): 48–53. http://dx.doi.org/10.4028/www.scientific.net/amm.643.48.

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Robotic automated fiber placement (Robotic AFP) was a cost-effective and highly innovative approach to produce large and complex composite structures. In order to achieve desired qualities, the tow tension of the process required to becontrolled accurately. Due to the high nonlinearity of the system, such as the large elastic modulus, flexibility and viscosity of the tow, traditional methods failed to work effectively. A fuzzy-adaptive PIDcontroller combining fuzzy logic and adaptive PID together was proposed in this work. Fuzzy logic was able to respond quickly to disturbances without the need for an accurate model and adaptive PID control could eliminate the steady-state error by adapting its parameters to the working conditions. With this method, the tow tension could be precisely regulated thus improved the qualities of the composite structures.

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50

Zhu, Gao Ke, Xiao Gang Duan, and Hua Deng. "Adaptive Fuzzy PID Force Control for a Prosthetic Hand." Applied Mechanics and Materials 433-435 (October 2013): 93–101. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.93.

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Abstract:

An adaptive fuzzy proportional-integral-derivative (PID) force control strategy for a prosthetic hand is presented. The classical PID controller is also applied on the prosthetic hand as comparison. The parameters of PID controller are firstly tuned by Cut and Try method. Then a fuzzy logic system is used to adjust those parameters on line. Real-time force control experiments are realized on LabVIEW and PXI (PCI eXtensions for Instrumentation) real-time (RT) platforms. A rigid object and a compliant object are grasped by the prosthesis respectively to test the performance of controllers. Experimental results indicate that the adaptive fuzzy PID force controller is more effective than PID controller.

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