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Добірка наукової літератури з теми "INVERTED PENDULUM SYSTEM"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "INVERTED PENDULUM SYSTEM"

1

Nasim, Shahzad, M. Javeed, M. Shafiq, Faraz Liaquat, and Zain Anwar Ali. "Self-Erected Inverted Pendulum." Advanced Materials Research 816-817 (September 2013): 415–19. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.415.

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The basic theme of this research paper is self-erecting the inverted pendulum by via ARDUINO controller and stabilizes the system through PID algorithm of linear control system. ARDUINO controller acquires the data from the sensors in terms of position and angle of the pendulum and commands the motor through PWM signal after that swing the pendulum from rest position to get and balance the inverted position. Controller read the pendulums angular position through potentiometer then calculates and removes errors via PID algorithm. MATLAB-Simulink and LABVIEW sent and receives runtime information from controller.

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2

Mesa, F., R. Ospina Ospina, and D. M. Devia-Narvaez. "Methodology of robust inverted pendulum controllers on a vehicle." Journal of Physics: Conference Series 2102, no. 1 (November 1, 2021): 012012. http://dx.doi.org/10.1088/1742-6596/2102/1/012012.

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Abstract In the theory of controllers, the simple and inverted pendulum play an important role due to the equations that result from them, which imply non-linearities and perturbations, thus, in this article, a brief classification of inverted pendulums is presented: inverted pendulum, inverted double pendulum, inverted rotary pendulum (Furuta pendulum). Subsequently, a mathematical model of the inverted pendulum is described through the deduction of the equations of motion that represent the dynamics of the system. Robust control is presented that allows expanding the richness of the mathematical equations, for this case, a control with output feedback is presented and applied to the inverted pendulum to control the unstable dynamics of this model. The results are compared with a post placement control and a robust control using a norm that analyses the characteristics of the system.

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3

Wang, Yujue, Weining Mao, Qing Wang, and Bin Xin. "Fuzzy Cooperative Control for the Stabilization of the Rotating Inverted Pendulum System." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 3 (May 20, 2023): 360–71. http://dx.doi.org/10.20965/jaciii.2023.p0360.

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The rotating inverted pendulum is a nonlinear, multivariate, strongly coupled unstable system, and studying it can effectively reflect many typical control problems. In this paper, a parameter self-tuning fuzzy controller is proposed to perform the balance control of a single rotating inverted pendulum. Particle swarm optimization is used to adjust its control parameters, and simulation experiments are performed to show that the system can achieve stability with the designed parametric self-tuning fuzzy controller, with control performance better than that of the conventional fuzzy controller. Furthermore, the leader-follower control strategy is used to realize the cooperative control of multiple rotating inverted pendulums. Two QUBE-Servo 2 rotating inverted pendulums are used for a cooperative pendulum swing-up experiment and stabilization experiment, and the effectiveness of the proposed cooperative control strategy is verified.

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4

PAGANO, DANIEL, LUIS PIZARRO, and JAVIER ARACIL. "LOCAL BIFURCATION ANALYSIS IN THE FURUTA PENDULUM VIA NORMAL FORMS." International Journal of Bifurcation and Chaos 10, no. 05 (May 2000): 981–95. http://dx.doi.org/10.1142/s0218127400000700.

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Анотація:

Inverted pendulums are very suitable to illustrate many ideas in automatic control of nonlinear systems. The rotational inverted pendulum is a novel design that has some interesting dynamics features that are not present in inverted pendulums with linear motion of the pivot. In this paper the dynamics of a rotational inverted pendulum has been studied applying well-known results of bifurcation theory. Two classes of local bifurcations are analyzed by means of the center manifold theorem and the normal form theory — first, a pitchfork bifurcation that appears for the open-loop controlled system; second, a Hopf bifurcation, and its possible degeneracies, of the equilibrium point at the upright pendulum position, that is present for the controlled closed-loop system. Some numerical results are also presented in order to verify the validity of our analysis.

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5

Sultan, Ghassan A., and Ziyad K. Farej. "Design and Performance Analysis of LQR Controller for Stabilizing Double Inverted Pendulum System." Circulation in Computer Science 2, no. 9 (October 20, 2017): 1–5. http://dx.doi.org/10.22632/ccs-2017-252-45.

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Double inverted pendulum (DIP) is a nonlinear, multivariable and unstable system. The inverted pendulum which continually moves toward an uncontrolled state represents a challenging control problem. The problem is to balance the pendulum vertically upward on a mobile platform that can move in only two directions (left or right) when it is offset from zero stat. The aim is to determine the control strategy that deliver better performance with respect to pendulum's angles and cart's position. A Linear-Quadratic-Regulator (LQR) technique for controlling the linearized system of double inverted pendulum model is presented. Simulation studies conducted in MATLAB environment show that the LQR controller are capable of controlling the multi output double inverted pendulum system. Also better performance results are obtained for controlling heavy driven part DIP system.

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6

Chawla, Ishan, and Ashish Singla. "ANFIS based system identification of underactuated systems." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 7-8 (November 18, 2020): 649–60. http://dx.doi.org/10.1515/ijnsns-2018-0005.

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AbstractIn this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.

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7

Wang, Hong Qi. "Dynamics Modeling of the Planar Double Inverted Pendulum." Applied Mechanics and Materials 195-196 (August 2012): 17–22. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.17.

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Анотація:

planar double inverted pendulum is a strong coupling, uncertain and complex nonlinear system, and the dynamics model of which is the basis of control, simulation and analysis. In the paper coordinate systems of the planar double inverted pendulum were first defined, and then the dynamics model of which was built up based on screw theory and the Lagrange principle. The modeling method used being systematic and standardized, it is easy to extend to dynamics modeling of higher order planar inverted pendulums or other multi-body systems.

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8

Sun, Qian Lai, and Zhi Yi Sun. "A Simple Control Strategy to Stabilize an Inverted Pendulum System." Advanced Materials Research 433-440 (January 2012): 3997–4002. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3997.

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Анотація:

A simple control strategy is presented to control An inverted pendulum. The control strategy is obtained via mathematical derivation based on the dynamical model of the inverted pendulum system. That control law is simple and independent of the model of the controlled plant. It is applicable for the multi input single output systems similar to inverted pendulum systems. A controller based on that method was designed to control an inverted pendulum. The structure of the controller is simple. And the parameter adjusting is relatively easy. Then the simulation study was realized. The simulation result shows that control law is valid for the inverted pendulum system.

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9

Lastomo, Dwi, Herlambang Setiadi, and Muhammad Ruswandi Djalal. "Design Controller of Pendulum System using Imperialist Competitive Algorithm." INTEK: Jurnal Penelitian 4, no. 1 (May 3, 2017): 53. http://dx.doi.org/10.31963/intek.v4i1.94.

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Анотація:

Due to development of technology in recent years, complexity and nonlinearity of mechanical and electrical system are increasing significantly. Inverted pendulum is nonlinear system that has become popular in recent years. However, inverted pendulum is nonlinear and unstable system. Therefore appropriate design controller of inverted pendulum system is crucial. Hence, this paper proposed, design of inverted pendulum system based on imperialist competitive algorithm (ICA). In order to design the controller, dynamic model of inverted pendulum system is used. Time domain simulation is used to address the controller performance. From the simulation result, it is found that imperialist competitive algorithm can be used to design inverted pendulum system controller.

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10

Samiee, Ahmad. "Optimal Control Comparisons on a Flywheel Based Inverted Pendulum." Mapta Journal of Mechanical and Industrial Engineering (MJMIE) 3, no. 1 (April 20, 2019): 18–26. http://dx.doi.org/10.33544/mjmie.v3i1.108.

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Анотація:

This paper introduces a comparison between two optimal controllers on a flywheel-based inverted pendulum. Inverted pendulums have an essential place in developing under-actuation nonlinear control schemes due to their nonlinear structure. This system is a basic structure for many advanced systems such as biped and mobile wheeled robots. Optimal controllers addressed in this paper consist of State-Dependent Riccati Equation (SDRE) and Linear Quadratic Regulator (LQR). A Proportional–Integral–Derivative controller (PID) is also designed and tested in the simulation. One axis self-balancing flywheel based inverted pendulum system is designed to validate the controllers' performance on an experimental setup.

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Дисертації з теми "INVERTED PENDULUM SYSTEM"

1

Oyama, Hiroshi, Takayuki Ukai, Hiroaki Takada, and Takuya Azumi. "Wheeled Inverted Pendulum with Embedded Component System : A Case Study." IEEE, 2010. http://hdl.handle.net/2237/14474.

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2

Cheang, Sek Un. "Robust control system design : H∞ loop shaping for double inverted pendulum." Thesis, University of Macau, 2002. http://umaclib3.umac.mo/record=b1445662.

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3

Maeda, Ken. "Nonlinear control system of inverted pendulum based on input-output linearization." Diss., Online access via UMI:, 2006.

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4

Gustavsson, Martin, and Viktor Frimodig. "Virtual Prototyping and Physical Validation of an Inverted Pendulum : "Sea-Calf Bot"." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-27946.

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The work is motivated by the goal of linking reality and model, and to see if there is an opportunity to develop an inexpensive educational tool for training in cyber-physical systems. This project has investigated the possibilities to build a cheap inverted pendulum with controller and connect this with the modeling language Acumen. Acumen models is used for comparison with the actual prototype. To solve these problems has a 3D printer been used to create hardware, Arduino UNO for control and Raspberry Pi for enable communication with Acumen over WLAN. The result was a cheap inverted pendulum, which can be built for a cost around 750 SEK. Graphs created in Acumen and from data collected from sensors can be analyzed. With a model of the inverted pendulum system, the results show that Acumen can be used in the development of cyber-physical systems. There are differences between model and reality but also similarities.
Arbetet motiveras av målet att knyta samman verklighet och modell, samt att se om det finns möjlighet att utveckla ett billigt utbildningsverktyg för utbildning i cyberfysiska system. Detta projekt har undersökt möjligheter att bygga en billig inverterad pendel med regulator samt koppla samman denna med modelleringsspråket Acumen. I Acumen skapa en modell av systemet och jämföra den med en fysisk prototyp. För att lösa dessa problem har en 3D skrivare använts för att skapa hårdvara. Arduino UNO för styrning och Raspberry Pi för att möjligöra kommunikation med Acumen över WLAN. Resultatet blev en billig inverterad pendel, som kan byggas för en kostnad runt 750 kr. Grafer från Acumen, och från data samlad från sensorer kan analyseras. Med en modell av en inverterad pendel visar resultaten att Acumen kan användas i utveckling av cyberfysiska system. Skillnader finns mellan modell och verklighet men även likheter.

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5

Phillips, Lara C. (Lara Christine). "Control of a dual inverted pendulum system using linear-quadratic and H-infinity methods." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36507.

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6

Bustamante, Montes Luis Gabriel. "Design and implementation of fuzzy logic and PID controllers to balance an inverted pendulum system." Scholarly Commons, 1994. https://scholarlycommons.pacific.edu/uop_etds/2267.

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Анотація:

A PID controller and a Fuzzy Logic controller were designed to balance an inverted pendulum system. Both controllers were implemented in a Digital Signal Processor (DSP). Measurements of the angular position of the pendulum (feedback signal) were taken from a precision potentiometer and transformed into digital by an Analog Interface Board (AlB) to be processed by the DSP. The DSP generated the digital control signal that was converted into analog by the AlB and then filtered and amplified to drive a DC motor. The DC motor provided the control force for the mobil base where the inverted pendulum was mounted. The PID controller was designed to move an unstable pole of the system from the tight side of the s-plane into the left side of the s-plane to provide stability and fast response. The Fuzzy Logic controller was designed using thirteen control rules that were generated using human intuition. It was found that the Fuzzy Logic controller required a considerably larger amount of memory than the PID controller. In general, the Fuzzy Logic controller performed better than the PID controller. It was concluded that nonlinearities present in some components of the system caused the PID controller not to perform as well. It was also found that the Fuzzy Logic controller was less sensitive to these nonlinearities, resulting in a better control of the inverted pendulum.

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7

Robillard, Dominic. "Development of a Stair-Climbing Robot and a Hybrid Stabilization System for Self-Balancing Robots." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31840.

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Self-balancing robots are unique mobile platforms that stay upright on two wheels using a closed-loop control system. They can turn on the spot using differential steering and have compact form factors that limit their required floor space. However they have major limitations keeping them from being used in real world applications: they cannot stand-up on their own, climb stairs, or overcome obstacles. They can fall easily if hit or going onto a slippery surface because they rely on friction for balancing. The first part of this research proposes a novel design to address the above mentioned issues related to stair-climbing, standing-up, and obstacles. A single revolute joint is added to the centre of a four-wheel drive robot onto which an arm is attached, allowing the robot to successfully climb stairs and stand-up on its own from a single motion. A model and simulation of the balancing and stair-climbing process are derived, and compared against experimental results with a custom robot prototype. The second part, a control system for an inverted pendulum equipped with a gyroscopic mechanism, was investigated for integration into self-balancing robots. It improves disturbance rejection during balance, and keeps equilibrium on slippery surfaces. The model of a gyroscope mounted onto an actuated gimbal was derived and simulated. To prove the concept worked, a custom-built platform showed it is possible for a balancing robot to stay upright with zero traction under the wheels.

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8

Houchin, Scott J. "Pendulum : controlling an inverted pendulum using fuzzy logic /." Online version of thesis, 1991. http://hdl.handle.net/1850/11294.

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9

Shao, Jindi. "Dynamics and nonlinear control of unstable inverted pendulum systems." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296946.

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10

Kong, Kou A. "Fuzzy logic PD control of a non-linear inverted flexible pendulum." [Chico, Calif. : California State University, Chico], 2009. http://hdl.handle.net/10211.4/90.

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Книги з теми "INVERTED PENDULUM SYSTEM"

1

Li, Zhijun, Chenguang Yang, and Liping Fan. Advanced Control of Wheeled Inverted Pendulum Systems. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2963-9.

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2

Li, Zhijun. Advanced Control of Wheeled Inverted Pendulum Systems. London: Springer London, 2013.

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3

Lam, Simon Sai-Ming. The real stability and stabilizability radii of the multi-link inverted pendulum system. 2005.

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4

Li, Zhijun, Chenguang Yang, and Liping Fan. Advanced Control of Wheeled Inverted Pendulum Systems. Springer, 2014.

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5

Advanced Control Of Wheeled Inverted Pendulum Systems. Springer, 2012.

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6

Barrett, Spencer Brown. Predictive control using feedback-: A case study of an inverted pendulum. 1995.

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7

Holzapfel, Frank G. Fuzzy logic control of an inverted pendulum with vision feedback. 1994.

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8

Inverted Pendulum in Control Theory and Robotics: From Theory to New Innovations. Institution of Engineering & Technology, 2017.

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9

Rajeev, S. G. Fluid Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805021.001.0001.

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Starting with a review of vector fields and their integral curves, the book presents the basic equations of the subject: Euler and Navier–Stokes. Some solutions are studied next: ideal flows using conformal transformations, viscous flows such as Couette and Stokes flow around a sphere, shocks in the Burgers equation. Prandtl’s boundary layer theory and the Blasius solution are presented. Rayleigh–Taylor instability is studied in analogy with the inverted pendulum, with a digression on Kapitza’s stabilization. The possibility of transients in a linearly stable system with a non-normal operator is studied using an example by Trefethen et al. The integrable models (KdV, Hasimoto’s vortex soliton) and their hamiltonian formalism are studied. Delving into deeper mathematics, geodesics on Lie groups are studied: first using the Lie algebra and then using Milnor’s approach to the curvature of the Lie group. Arnold’s deep idea that Euler’s equations are the geodesic equations on the diffeomorphism group is then explained and its curvature calculated. The next three chapters are an introduction to numerical methods: spectral methods based on Chebychev functions for ODEs, their application by Orszag to solve the Orr–Sommerfeld equation, finite difference methods for elementary PDEs, the Magnus formula and its application to geometric integrators for ODEs. Two appendices give an introduction to dynamical systems: Arnold’s cat map, homoclinic points, Smale’s horse shoe, Hausdorff dimension of the invariant set, Aref ’s example of chaotic advection. The last appendix introduces renormalization: Ising model on a Cayley tree and Feigenbaum’s theory of period doubling.

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Частини книг з теми "INVERTED PENDULUM SYSTEM"

1

Gu, Da-Wei, Petko H. Petkov, and Mihail M. Konstantinov. "A Triple Inverted Pendulum Control System Design." In Robust Control Design with MATLAB®, 291–325. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4682-7_15.

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2

Du, Dajun, Bin Zhan, Wangpei Li, Minrui Fei, and TaiCheng Yang. "Experimental Analysis of Visual Inverted Pendulum Servoing System." In Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, 441–50. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2669-0_47.

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3

Alam, Md Shah, and Sharmistha Mandal. "LMI Based Robust Control of Inverted Pendulum System." In Learning and Analytics in Intelligent Systems, 420–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42363-6_49.

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4

Ronquillo, G., G. J. Ríos Moreno, E. Hernández Martínez, and M. Trejo Perea. "Nonlinear Identification of Inverted Pendulum System Using Volterra Polynomials." In Multibody Mechatronic Systems, 87–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09858-6_9.

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5

Kharola, Ashwani, Rahul, and Varun Pokhriyal. "Adaptive Neuro Fuzzy Control of Triple Inverted Pendulum System." In Lecture Notes in Electrical Engineering, 279–87. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0969-8_28.

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6

Gao, Hongxia, Yun Lu, Qian Mai, and Yueming Hu. "Inverted Pendulum System Control by Using Modified Iterative Learning Control." In Intelligent Robotics and Applications, 1230–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10817-4_123.

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7

Ding, Zhaohong. "Design of Mamdani Cascade Fuzzy Control System for Inverted Pendulum." In Lecture Notes in Electrical Engineering, 1869–75. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_204.

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8

Abu, Yizhak, Tom Hirshberg, and Alex M. Bronstein. "Data-Driven Control of an Inverted Pendulum System^*." In 2023 Proceedings of the Conference on Control and its Applications (CT), 80–86. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977745.11.

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9

Chawla, Ishan, and Ashish Singla. "System Identification of an Inverted Pendulum Using Adaptive Neural Fuzzy Inference System." In Harmony Search and Nature Inspired Optimization Algorithms, 809–17. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_77.

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10

Wu, Yu, and Peiyi Zhu. "Fuzzy Control for the Swing-Up of the Inverted Pendulum System." In Communications in Computer and Information Science, 454–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18134-4_73.

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Тези доповідей конференцій з теми "INVERTED PENDULUM SYSTEM"

1

Panya, Samatthachai, Taworn Benjanarasuth, Songmoung Nundrakwang, Jongkol Ngamwiwit, and Noriyuki Komine. "Hybrid Controller for Inverted Pendulum System." In 2008 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2008. http://dx.doi.org/10.1109/iscit.2008.4700219.

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2

Le, Tony, and Paul Oh. "NXT Mobile Inverted Pendulum." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49667.

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The intent of this paper is to provide information on how to implement a mobile inverted pendulum using the LEGO® Mindstorms NXT platform for educational purposes in mechatronics. A description of the dynamics of a mobile inverted pendulum is first, followed by a description of the hardware and software components composing the NXT platform. Discussed are the capabilities and the limitations of the NXT system. As a demonstration, a mobile inverted pendulum is built and controlled using a simple PID controller. Sensors used include a HiTechnic gyro sensor to measure angular rate for balancing and the NXT ultrasound sensor for obstacle avoidance. Shown are the simulated and experimental results of the angular rate and velocity control. Lastly, a breakdown of a hypothetical course in mechatronics highlights the described NXT mobile inverted pendulum.

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Pannil, Pittaya, Aroosh Klaeoyotha, Prapart Ukakimaparn, Thanit Trisuwannawat, Kitti Tirasesth, and Noriyuki Kominet. "Development of Inverted Pendulum System at KMITL." In 2008 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2008. http://dx.doi.org/10.1109/iscit.2008.4700220.

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4

Song, Yang, Jin-xia Xie, Yi-qing Shi, and Li Jia. "Switching stabilization for the inverted pendulum system." In 2009 Chinese Control and Decision Conference (CCDC 2009). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5191582.

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5

Roman, M., E. Bobasu, and D. Sendrescu. "Modelling of the rotary inverted pendulum system." In 2008 IEEE International Conference on Automation, Quality and Testing, Robotics. IEEE, 2008. http://dx.doi.org/10.1109/aqtr.2008.4588810.

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6

Resende, Diogo, Marcus Vinicius Girão de Morais, and Suzana Avila. "VIBRATION CONTROL USING AN INVERTED PENDULUM SYSTEM." In DINAME2019. ABCM, 2019. http://dx.doi.org/10.26678/abcm.diname2019.din2019-0107.

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Li, Qing-Rui, Wen-Hua Tao, Na Sun, Chong-Yang Zhang, and Ling-Hong Yao. "Stabilization Control of Double Inverted Pendulum System." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.662.

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Balkan, Tuna, and Mehmet Emin Ari. "Fuzzy Control of an Inverted Pendulum." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/cie-1441.

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Анотація:

Abstract An inverted pendulum system has been designed and constructed as a physical model of inherently unstable mechanical systems. The vertical upright position of a pendulum is controlled by changing the horizontal position of a cart to which the pendulum is hinged. The stability of the system has been investigated when a fuzzy controller is used to produce the control signal, while making a single measurement. It has been shown that by using simple fuzzy rules to allow real time computation with a single angular position measurement, the system can not be made absolutely stable. However, the stability and performance of the system have been considerably improved by shrinking the membership functions of angular position, computed angular velocity and control signal when inverted pendulum is very close to the vertical upright position.

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Chiu, Chih-Hui, and Ya-Fu Peng. "The implementation of a rotary inverted pendulum." In 2018 IEEE International Conference on Applied System Innovation (ICASI). IEEE, 2018. http://dx.doi.org/10.1109/icasi.2018.8394441.

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Wei, Fu, Giang Liangzhong, Yang Jin, and Bian Qingqing. "Inverted Pendulum Control System Based on GA Optimization." In Workshop on Intelligent Information Technology Application (IITA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iita.2007.85.

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Звіти організацій з теми "INVERTED PENDULUM SYSTEM"

1

Seto, Danbing, and Lui Sha. A Case Study on Analytical Analysis of the Inverted Pendulum Real-Time Control System. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada373286.

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