Relevant bibliographies by topics / On-line tuning PID controller

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  2. Dissertations / Theses
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Academic literature on the topic 'On-line tuning PID controller'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "On-line tuning PID controller"

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Chen, Wen Wen, You Kuan Liu, Xiang Yu Tan, et al. "PID Parameter Optimization Based on Fuzzy Control." Advanced Materials Research 960-961 (June 2014): 1156–61. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1156.

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PID controllers are widely used in industrial process control. The determination of the conventional PID controller parameter tuning is based on obtaining the mathematical model of controlled objects and according to certain rules, which is difficult to adapt to complex control systems. In this paper ,against its adverse effect parameter tuning, long time debugging, defects and poor adaptability of the controlled object,the fuzzy control and PID control are combined and the fuzzy PID controller is proposed. Then, I combine the examples of thermal power units using MATLAB to simulate. The simul
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Riaño Jaimes, Cristhian Ivan, Jorge Luis Diaz Rodriguez, and Diego Armando Mejía Bugallo. "On-line method for optimal tuning of PID controllers using standard OPC interface." Inge CuC 18, no. 2 (2022): 13–26. http://dx.doi.org/10.17981/ingecuc.18.2.2022.02.

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Introduction− The controlled PID is the most widely used mathematical algorithm as a regulatory control strategy in industrial environments. The applications are varied; however, its answer depends on the proper calculation of its three parameters: the proportional, the derivative, and the integral. Analytical tuning and experimental methods solve the problem, but new tuning possibilities are now enabled within the digital and process integration context. Objective− Automatically and remotely obtain the optimal parameters of the PID controller, taking advantage of an online connection via the
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Liu, Chan Yuan, and Guo Guang Chen. "Based on the MATLAB Self-Tuning Fuzzy PID Controller Design." Applied Mechanics and Materials 313-314 (March 2013): 443–47. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.443.

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Based on MATLAB self-tuning fuzzy PID controller design, the line parameter of the PID controller to adjust, to further improve the performance of the PID controller to adapt to changes in the control object parameters changes and working conditions.Introduction
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Chen, Yin Ping. "Study on Auto-Tuning PID Controller." Advanced Materials Research 1037 (October 2014): 225–27. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.225.

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PID control is the most common control method used in process control. The PID control parameters tuning methods are develop constantly. At present, in numerous tuning methods, there are mainly two methods applied better in the practical industrial process. One is based on pattern identification (based on rules); the other is based on relay feedback (based on model). They are collectively referred to as intelligent PID parameter tuning method. This paper studies on the PID parameter auto-tuning methods and introduces the results of the latest research on this subject. Finally, the development
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Arvanitis, K. G., N. A. Sigrimis, G. D. Pasgianos, and G. Kalogeropoulos. "On-line PID controller tuning for open-loop unstable processes." IFAC Proceedings Volumes 33, no. 20 (2000): 25–32. http://dx.doi.org/10.1016/s1474-6670(17)38020-5.

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Tan, K. K., T. H. Lee, and X. Jiang. "On-line relay identification, assessment and tuning of PID controller." Journal of Process Control 11, no. 5 (2001): 483–96. http://dx.doi.org/10.1016/s0959-1524(00)00012-3.

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Sheet, Amer Farhan. "Optimization of DC motor speed control based on fuzzy logic-PID controller." Analysis and data processing systems, no. 3 (September 30, 2021): 143–53. http://dx.doi.org/10.17212/2782-2001-2021-3-143-153.

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In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a cha
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Niembro-Ceceña, José A., Roberto A. Gómez-Loenzo, Juvenal Rodríguez-Reséndiz, Omar Rodríguez-Abreo, and Ákos Odry. "Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control." Micromachines 13, no. 8 (2022): 1264. http://dx.doi.org/10.3390/mi13081264.

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Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due
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Vrančić, Damir, and Mikuláš Huba. "High-Order Filtered PID Controller Tuning Based on Magnitude Optimum." Mathematics 9, no. 12 (2021): 1340. http://dx.doi.org/10.3390/math9121340.

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The paper presents a tuning method for PID controllers with higher-order derivatives and higher-order controller filters (HO-PID), where the controller and filter orders can be arbitrarily chosen by the user. The controller and filter parameters are tuned according to the magnitude optimum criteria and the specified noise gain of the controller. The advantages of the proposed approach are twofold. First, all parameters can be obtained from the process transfer function or from the measured input and output time responses of the process as the steady-state changes. Second, the a priori defined
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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 (2002): 60–67. http://dx.doi.org/10.1049/ip-cta:20020099.

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Dissertations / Theses on the topic "On-line tuning PID controller"

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Ngo, Thanh Binh. "Design and creation of control board for drying equipment based on development of a soft self-tuning PID controller." Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A32721.

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This article introduces a versatile control board design that can be used in many drying systems using direct heat transfer solutions in combination with static tray distribution based on development of a flexible self-tuning PID controller. The product is applied for a small oven drying solid waste samples or plant materials for further analysis of some parameters. The control board is built based on the Arduino embedded system using a flexible soft PID (Proportional–Integral–Derivative) controller that can automatically change its gains according to the required temperature thresholds to bes
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Gaber, Mark Rawi. "Off-line tuning of PID controllers for systems subjected to random disturbances." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61239.

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An off-line method of tuning PID controllers used in process control applications is presented. The plant under consideration is of second order with delay and the loop is presumed to be operating at steady state under the influence of random disturbances. Tuning is performed by minimizing the cost function, namely the integral of squared error, with respect to the tuning parameters. To do this, a second order approximation of the cost function is obtained and minimized using the Newton-Raphson method. System identification is done without interrupting production by introducing test signals of
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Pektas, Seda. "On-line Controller Tuning By Matlab Using Real System Responses." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605596/index.pdf.

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This thesis attempts to tune any controller without the mathematical model knowledge of the system it is controlling. For that purpose, the optimization algorithm of MATLAB&reg<br>6.5 / Nonlinear Control Design Blockset (NCD) is adapted for real-time executions and combined with a hardware-in-the-loop simulation provided by MATLAB&reg<br>6.5 / Real-Time Windows Target (RTWT). A noise-included model of a DC motor position control system is obtained in MATLAB&reg<br>/ SIMULINK first and simulated to test the modified algorithm in some aspects. Then the presented methodology is verified using the
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Mustafa, Mohd Marzuki bin. "Pole-assignment high order controllers and applications to adaptive control with on-line supervision." Thesis, University of Salford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.258358.

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Wang, Zimu. "A MODEL OF LINE FOLLOWING ROBOT USING PID CONTROLLER : An Educational Platform Based on LEGO Mindstorms NXT Kit." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-21428.

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PID controllers are widely used in industrial control systems since it could significantly improve the performance of the systems. It is a basic and important tool for an engineering student.An educational PID controller model using LEGO Mindstorms NXT kit as a line following robot was presented in this thesis. This robot could be controlled by a PC via Bluetooth. MATLAB was used for analyzing data and controlling the robot. A MATLAB Toolbox named “RWTH—Mindstorms NXT Toolbox” was utilized for remote controlling and gathering the feedback data from the robot in real-time via Bluetooth.The algo
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CONGIU, ANDREA. "Novel load identification techniques and a steady state self-tuning prototype for switching mode power supplies." Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266433.

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Control of Switched Mode Power Supplies (SMPS) has been traditionally achieved through analog means with dedicated integrated circuits (ICs). However, as power systems are becoming increasingly complex, the classical concept of control has gradually evolved into the more general problem of power management, demanding functionalities that are hardly achievable in analog controllers. The high flexibility offered by digital controllers and their capability to implement sophisticated control strategies, together with the programmability of controller parameters, make digital control very attracti
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Chou, Chin Jui, and 周進瑞. "Studies of On-Line PID Controller Tuning Techniques." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/46091694860149878984.

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碩士<br>東海大學<br>化學工程研究所<br>81<br>Three kinds of closed-loop PID tuning techniques including time domain, frequency domain, and cascade control approaches are proposed in this study. Under proportional control, a single dynamic test ( such as step or pulse change ) in the set point is introduced to the system. An identification technique for the underdamped second-order-plus-dead-time model from few data points of the step response curve is developed. The ultimate frequency and
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Chen, Ping-Lin, and 陳秉霖. "On-line Tuning PID Controller Based on Swarm Intelligence." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/03052568016266430530.

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碩士<br>國立東華大學<br>電機工程學系<br>99<br>The main focus of this thesis is to design the intelligent controller, and discuss the system tracking control problem with disturbance influence. This thesis presents a Particle Swarm Optimization (PSO) approach for online tuning Proportional Integral Derivative (PID) controller. There is a great deal of general literature on PID controller with ignoring the disturbance interference. However, in practical applications, the ability of interference avoidance of PID controller should be considered in evaluating the performance of system response. In this resea
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Huang, Tien-Chih, and 黃天志. "On-Line Self-Tuning PID Controller Design Based on Neural Network Models." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/50358439153686754176.

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碩士<br>中原大學<br>化學工程研究所<br>90<br>The inherent time-varying nonlinearity and complexity usually exist in chemical processes. The design process would be significantly deviated from the normal operating condition when only a slight disturbance occurs. Accordingly, the design of control structure should be properly adapted based on the instantaneous state. In this research, an improved conventional PID control scheme using linearization through a specified neural network is developed to control nonlinear processes. An input-driven output neural network ARX model trained by Levenberg-Marquardt algo
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Chou, Yu-Wei, and 邱昱瑋. "On-line PID-Controller Parameter Tuning Using SPSA for Wind Turbine Yaw Control." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/39865824294741711926.

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碩士<br>中原大學<br>電機工程研究所<br>95<br>It’s a very important and worthy to discuss how to use active yaw control to position the maximum power point with a horizontal axis wind turbine which usually needs wind direction sensor to position. The mountain climbing method based on measurement of the voltage and current from wind-turbine generator system (WTGS) to calculate the power for the maximum power point without any wind direction sensors is proposed. It’s verified by computer simulations and experiments. An on-line parameter tuning PID controller for DC motor to position the wind turbine is also
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Books on the topic "On-line tuning PID controller"

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United States. Army Aviation Research and Technology Activity. Propulsion Directorate. and United States. National Aeronautics and Space Administration., eds. An expert system to perform on-line controller tuning. National Aeronautics and Space Administration, 1990.

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Book chapters on the topic "On-line tuning PID controller"

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Arun, M. K., U. Biju, Neeraj Nair Rajagopal, and V. Bagyaveereswaran. "Optimal Tuning of Fractional-Order PID Controller." In Proceedings of the International Conference on Soft Computing Systems. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2671-0_38.

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Adhikari, Nikhileshwar Prasad, and Amit Gupta. "PID Controller Tuning Using Soft Computing Techniques." In Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012). Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0970-6_31.

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Papadopoulos, Konstantinos G. "Changes on the Current State of the Art." In PID Controller Tuning Using the Magnitude Optimum Criterion. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07263-0_8.

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Yin, Jianchuan, Gexin Bi, and Fang Dong. "On-Line Tuning of a Neural PID Controller Based on Variable Structure RBF Network." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_124.

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Tello-Rdz, Heberi R., Luis M. Torres-Treviño, and Angel Rodríguez-Liñan. "Off-line Tuning of a PID Controller Using Type-2 Fuzzy Logic." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62434-1_22.

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Chen, Le, Baoming Ge, and Aníbal T. de Almeida. "Self-tuning PID Temperature Controller Based on Flexible Neural Network." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_18.

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Suguna, A., S. N. Deepa, and N. Rajasingam. "Modeling and Tuning of PID Controller for Continuous Stirred Tank Reactor." In International Conference on Artificial Intelligence for Smart Community. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2183-3_77.

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Zhao, Jun, Tianpeng Li, and Jixin Qian. "Application of Particle Swarm Optimization Algorithm on Robust PID Controller Tuning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_118.

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Su, Kuifeng, Tianqing Chang, Bin Zhu, and Bin Han. "PID Tuning for LOS Stabilization System Controller Based on BBO Algorithm." In Proceedings of the 2015 Chinese Intelligent Automation Conference. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46463-2_54.

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Xu, Ping, Geng Li, and Kai Wang. "Self Tuning of PID Controller Based on Simultaneous Perturbation Stochastic Approximation." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27287-5_105.

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Conference papers on the topic "On-line tuning PID controller"

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Nam, Ngo Hong, Truong Nguyen Luan Vu, and Vo Lam Chuong. "PID Controller Tuning Rules for Feed Drive Systems." In 2024 7th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2024. http://dx.doi.org/10.1109/gtsd62346.2024.10675245.

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Bhuvaneswary, N., B. Gowtham Reddy, K. Praveen Kumar Reddy, M. Vishnu Vardhan, K. Jagadeesh Kumar, and M. Anudeep Venkat Reddy. "Quadcopter with Fuzzy Logic based Self-Tuning PID Controller." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988349.

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Shah, Udayan, Nitesh Kumar Soni, Mitram Pandey, Rajveer Singh Jadon, and Yash Bairagi. "Elevating BLDC Motor Performance with Precision PID Controller Tuning." In 2024 IEEE Third International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 2024. http://dx.doi.org/10.1109/icpeices62430.2024.10719210.

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Doicin, Bogdan, Marian Popescu, and Cristian Patrascioiu. "PID Controller optimal tuning." In 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2016. http://dx.doi.org/10.1109/ecai.2016.7861175.

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Koivo, H., and J. Sorvari. "On-line tuning of a multivariable PID-controller for robot manipulators." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268765.

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Gade, S. S., S. B. Shendage, and M. D. Uplane. "On Line Auto Tuning of PID Controller Using Successive Approximation Method." In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (ITC 2010). IEEE, 2010. http://dx.doi.org/10.1109/itc.2010.22.

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Paulusova, Jana, Lukas Orlicky, and Maria Dubravska. "Self-tuning fuzzy PID controller." In 2013 International Conference on Process Control (PC). IEEE, 2013. http://dx.doi.org/10.1109/pc.2013.6581427.

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Sukede, Abhijeet Kishorsingh, and Jasmineet Arora. "Auto tuning of PID controller." In 2015 International Conference on Industrial Instrumentation and Control (ICIC). IEEE, 2015. http://dx.doi.org/10.1109/iic.2015.7150979.

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Fabijanski, Pawel, and Ryszard Lagoda. "On-line PID controller tuning using genetic algorithm and DSP PC board." In 2008 13th International Power Electronics and Motion Control Conference (EPE/PEMC 2008). IEEE, 2008. http://dx.doi.org/10.1109/epepemc.2008.4635574.

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Jones, K. O. "On-line control of dissolved oxygen concentration using an automatic tuning PID controller." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940279.

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Reports on the topic "On-line tuning PID controller"

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Lai, Chin-Ta, and Joel Conte. Dynamic Modeling of the UC San Diego NHERI Six-Degree-of-Freedom Large High-Performance Outdoor Shake Table. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2024. http://dx.doi.org/10.55461/jsds5228.

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The UC San Diego Large High-Performance Outdoor Shake Table (LHPOST), which was commissioned on October 1, 2004 as a shared-use experimental facility of the National Science Foundation (NSF) Network for Earthquake Engineering Simulation (NEES) program, was upgraded from its original one degree-of-freedom (LHPOST) to a six degree-of-freedom configuration (LHPOST6) between October 2019 and April 2022. The LHPOST6 is a shared-use experimental facility of the NSF Natural Hazard Engineering Research Infrastructure (NHERI) program. A mechanics-based numerical model of the LHPOST6 able to capture the
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