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Journal articles on the topic 'Ball and beam system'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Ho, Chao Ching, and C. L. Shih. "Machine Vision Based Tracking Control of a Ball-Beam System." Key Engineering Materials 381-382 (June 2008): 301–4. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.301.

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The dynamic behavior of a ball-beam system is highly nonlinear and its characteristic is difficult to define. In this paper we present a new ball-beam balancing control system using machine vision to feedback the beam angle and ball position on the beam. Adaptive threshold based continuously mean shift vision tracking algorithm is applied to record the ball position and the beam angle with highly captured frame-rate. The proposed vision tracking algorithm is tolerant to lighting influence, highly computing efficiency and more robust than traditional template pattern matching or edge detection algorithm under non-ideal environment. The vision tracking performance is experimentally tested on a ball-beam benchmark system, where a PD controller is applied to control the motion of the ball to maintain balance. Experimental result shows that the beam angle measurement, ball tracking and balancing control of the vision feedback system are robust, accurate and highly efficient.

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2

Colón, Diego, Átila Madureira Bueno, Yuri Smiljanic Andrade, Ivando Severino Diniz, and José Manoel Balthazar. "Nonlinear Ball and Beam Control System Identification." Applied Mechanics and Materials 706 (December 2014): 69–80. http://dx.doi.org/10.4028/www.scientific.net/amm.706.69.

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The Ball and Beam system is a common didactical experiment in control laboratories that can be used to illustrate many different closed-loop control techniques. The plant itself is subjected to many nonlinear effects, which the most common comes from the relative motion between the ball and the beam. The modeling process normally uses the lagrangean formulation. However, many other nonlinear effects, such as non-viscous friction, beam flexibility, ball slip, actuator elasticity, collisions at the end of the beam, to name a few, are present. Besides that, the system is naturally unstable. In this work, we analyze a subset of these characteristics, in which the ball rolls with slipping and the friction force between the ball and the beam is non-viscous (Coulomb friction). Also, we consider collisions at the ends of the beam, the actuator consists of a (rubber made) belt attached at the free ends of the beam and connected to a DC motor. The model becomes, with those nonlinearities, a differential inclusion system. The elastic coefficients of the belt are experimentally identified, as well as the collision coefficients. The nonlinear behavior of the system is studied and a control strategy is proposed.

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3

Lv, Xiao Hu, Yong Xin Liu, Yu Liu, and Hai Yan Huang. "Design of Ball-Beam Control System Based on Machine Vision." Applied Mechanics and Materials 71-78 (July 2011): 4219–25. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.4219.

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The nonlinear Ball-beam system combine with a CCD camera is studied in this paper. The images which include Ball-beam system and a ruler are collected by CCD sensor. The image is segmented using the adaptive image binarization threshold algorithm, and then the ruler, the ball position and the pointer position are extracted from the image. The ruler is scaled and the pointer position is also calculated. Finally, the value of pointer position is input into Ball-beam system as an expected ball balance position. A Fuzzy self-tuning PID controller and a BP neural network PID controller are designed for ball balance stable control. After experimental, the Ball-beam balance control in any position can be fulfilled using both of algorithms.

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4

Šitum, Željko, and Joško Petrić. "A Pneumatically Actuated Ball and Beam System." International Journal of Mechanical Engineering Education 36, no. 3 (July 2008): 225–34. http://dx.doi.org/10.7227/ijmee.36.3.6.

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5

Hamed, Basil. "Application of a LabVIEW for Real-Time Control of Ball and Beam System." International Journal of Engineering and Technology 2, no. 4 (2010): 401–7. http://dx.doi.org/10.7763/ijet.2010.v2.155.

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6

Kharola, Ashwani, and Pravin P. Patil. "Neural Fuzzy Control of Ball and Beam System." International Journal of Energy Optimization and Engineering 6, no. 2 (April 2017): 64–78. http://dx.doi.org/10.4018/ijeoe.2017040104.

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This paper presents an offline control of ball and beam system using fuzzy logic. The objective is to control ball position and beam orientation using fuzzy controllers. A Matlab/Simulink model of the proposed system has been designed using Newton's equations of motion. The fuzzy controllers were built using seven gbell membership functions. The performance of proposed controllers was compared in terms of settling time, steady state error and overshoot. The simulation results are shown with the help of graphs and tables which illustrates the effectiveness and robustness of proposed technique.

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7

Ulegin, К. А., K. N. Shvedov, А. N. Borodin, and V. Yu Rubtsov. "The new ball-rolling mill of EVRAZ NTMK – new possibilities for customers." Ferrous Metallurgy. Bulletin of Scientific , Technical and Economic Information 76, no. 6 (July 21, 2020): 602–8. http://dx.doi.org/10.32339/0135-5910-2020-6-602-608.

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In view of increase of ferrous and nonferrous ores mining, the need for grinding balls, used for the ores grinding in the process of concentration, is also increasing. Description of technical solutions, realized at accomplishment of an investment project at JSC EVRAZ NTMK, presented. The investment project on the technical modification of the rail and beam shop included construction of the new ball-rolling mil further to production of steel grinding balls. The new ball-rolling mill, which was put into operation in 2018, has a possibility to produce ball of diameter from 60 up to 120 mm with hardness up to 5th group. The mill is equipped by an automation system of the process and balls stocking control. The ball production section comprises a walking beams heating furnace with automated heating modes to ensure energy-efficient operation; automated hot rolling mill for production grinding balls of 60 ‒ 120 mm diameter; balls thermal treatment line, including temperature leveling facility, quenching machine and tempering furnace. The system of the process automation enables to trace on-line all the technological parameters for production quality products, parameters of the environment to increase the energy efficiency of the section, and control system to create safety working area at the section. Within a year after the ball-rolling mill commissioning, the whole nomenclature series of balls was mastered and the passport productivity at each profile was reached. The maximum mill productivity obtained was 22 t/h. The JSC EVRAZ NTMK mill of grinding balls of 60 ‒ 120 mm diameter production meets all the modern market requirements.

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8

Kharola, Ashwani, and Pravin P. Patil. "Soft-Computing Control of Ball and Beam System." International Journal of Applied Evolutionary Computation 9, no. 4 (October 2018): 1–21. http://dx.doi.org/10.4018/ijaec.2018100101.

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This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

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9

Yu, Wen. "Nonlinear PD Regulation for Ball and Beam System." International Journal of Electrical Engineering & Education 46, no. 1 (January 2009): 59–73. http://dx.doi.org/10.7227/ijeee.46.1.5.

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10

Al-Dujaili, Ayad Q., Amjad J. Humaidi, Daniel Augusto Pereira, and Ibraheem Kasim Ibraheem. "Adaptive backstepping control design for ball and beam system." International Review of Applied Sciences and Engineering 12, no. 3 (July 21, 2021): 211–21. http://dx.doi.org/10.1556/1848.2021.00193.

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AbstractBall and Beam system is one of the most popular and important laboratory models for teaching control systems. This paper proposes a new control strategy to the position control for the ball and beam system. Firstly, a nonlinear controller is proposed based on the backstepping approach. Secondly, in order to adapt online the dynamic control law, adaptive laws are developed to estimate the uncertain parameters. The stability of the proposed adaptive backstepping controller is proved based on the Lyapunov theorem. Simulated results are presented to illustrate the performance of the proposed approach.

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11

Krasinskii, A. Ya, A. N. Il'ina, and E. M. Krasinskaya. "Modeling of the Ball and Beam system dynamics as a nonlinear mechatronic system with geometric constraint." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki 27, no. 3 (September 2017): 414–30. http://dx.doi.org/10.20537/vm170310.

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12

Burghardt, Andrzej, and Marcin Szuster. "Neuro-Dynamic Programming in Control of the Ball and Beam System." Solid State Phenomena 210 (October 2013): 206–14. http://dx.doi.org/10.4028/www.scientific.net/ssp.210.206.

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This paper presents a new approach to the control problem of the ball and beam system, with a Neuro-Dynamic Programming algorithm implemented as the main part of the control system. The controlled system is included in the group of underactuated systems, which are nonlinear dynamical objects with the number of control signals smaller than the number of degrees of freedom. This results in problems in the formulation of a stable control algorithm, that guarantees stabilization of the ball in the desired position on the beam. The type of ball and beam material has a noticeable influence on the difficulties in stabilization of the ball, because of a smaller rolling friction and big inertia of the used metallic ball in comparison to other, for example made of non-metallic materials. The main part of the proposed discrete control system is the Neuro-Dynamic Programming algorithm in a Dual-Heuristic Dynamic Programming configuration, realized in a form of two neural networks: the actor and the critic. Neuro-Dynamic Programming algorithms use the Reinforcement Learning idea for adaptation of artificial neural network weights. Additional elements of the control system are the PD controller and the supervisory term, that ensures stability of the closed system loop. The control algorithm works on-line and does not require a preliminary learning phase of the neural network weights. Performance of the control algorithm was verified using the physical system controlled by the dSpace digital signal processing board.

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13

Ali, Haytem, Abdulgani Albagul, and Alhade Algitta. "OPTIMIZATION OF PID PARAMETERS BASED ON PARTICLE SWARM OPTIMIZATION FOR BALL AND BEAM SYSTEM." International Journal of Engineering Technologies and Management Research 5, no. 9 (March 21, 2020): 59–69. http://dx.doi.org/10.29121/ijetmr.v5.i9.2018.289.

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This paper introduces the application of an optimization technique, known as Particle Swarm Optimization (PSO) algorithm to the problem of tuning the Proportional-Integral-Derivative (PID) controller for a linearized ball and beam control system. After describing the basic principles of the Particle Swarm Optimization, the proposed method concentrates on finding the optimal solution of PID controller in the cascade control loop of the Ball and Beam Control System. Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. The efficiency of Particle Swarm Optimization algorithm for tuning the controller will be compared with a classical method, Trial and Error method. The comparison is based on the time response performance. The two tuning methods have been developed by simulation study using Matlab\ m-file software. The evaluations show that Evolutionary method Particle Swarm Optimization (PSO) algorithm gives a much better response than trial and error method.

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14

Muškinja, Nenad, and Matej Rižnar. "Optimized PID Position Control of a Nonlinear System Based on Correlating the Velocity with Position Error." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/796057.

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We examined a design approach for a PID controller for a nonlinear ball and beam system. Main objective of our research was to establish a nonmodel based control system, which would also not be dependent on a specific ball and beam hardware setup. The proposed PID controller setup is based on a cascaded configuration of an inner PID ball velocity control loop and an outer proportional ball position control loop. The effectiveness of the proposed controller setup was first presented in simulation environment in comparison to a hardware dependent PD cascaded controller, along with a more comprehensive study on possible design approach for optimal PID controller parameters in relation to main functionality of the controller setup. Experimental real time control results were then obtained on a laboratory setup of the ball and beam system on which PD cascaded controller could not be applied without parallel system model processing.

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15

KINOSHITA, Dai, and Kazunobu YOSHIDA. "Stabilizing Control for a Ball and Beam System Considering Restricted Beam Angle." Transactions of the Society of Instrument and Control Engineers 57, no. 6 (2021): 285–92. http://dx.doi.org/10.9746/sicetr.57.285.

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16

Ali, Tayyab, Suheel Abdullah Malik, Muhammad Adeel, and Muhammad Amir. "Set point tracking of Ball and Beam System Using Genetic Algorithm based PI-PD Controller." NUST Journal of Engineering Sciences 11, no. 1 (March 10, 2019): 12–16. http://dx.doi.org/10.24949/njes.v11i1.287.

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The ball and beam system is one of the commonly used benchmark control apparatus for evaluating numerous different real systems and control strategies. It is an inherently nonlinear and open-loop unstable system. In this paper, we have suggested an Evolutionary Algorithm (EA) based Proportional Integral-Proportional Derivative (PI-PD) controller for the set point tracking of this well-known ball and beam system. A linearized model of the ball and beam system is deduced and PI-PID control methodology is employed. The popular EA technique such as Genetic algorithm (GA) is used for tuning of the controller. The optimized values of the controller parameters are achieved by solving a fitness function using GA. The transient performance of the proposed GA based PI-PD controller (GA-PI-PD) is evaluated by carrying set point tracking analysis of the ball and beam system through MATLAB/Simulink simulations. Furthermore, the performance of GA-PI-PD controller is investigated using four different performance indices such as Integral of squared value of error (ISE), Integral of time multiplied by squared value of error (ITSE), Integral of absolute value of error (IAE) and Integral of time multiplied by absolute value of error (ITAE). The comparison of transient performance including rising time, settling time and % overshoot is made with SIMC-PID and H-infinity controllers. The comparison reveals that GA-PI-PD controller yielded transient response with small % overshoot and settling time. The superior performance of the GA-PI-PD controller has witnessed that it is highly effective for maintaining good stability and the setpoint tracking of ball and beam system with fast settling time and less overshoot than SIMC-PID and H-infinity controllers.

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17

Wang, Jian Wei, and Hui Xu. "New Strategy Suppressing Vibration for Euler Beam by Using Impact Damping of Inlaid Steel Ball." Advanced Materials Research 156-157 (October 2010): 1122–28. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.1122.

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A new passive vibration control strategy has been put forward and applied to a cantilever beam in this paper. For the beam under principal resonant harmonic excitations, a steel ball was inlaid in its interior closed cavity without any restricts, constructing an impact system. For the system, a spring-damping impact model was employed to simulate impacts between the beam and the steel ball, and dynamic governing equations ware derived. Numerical simulations show that the amplitude of the beam vibration response is significantly reduced by the impact damping of the steel ball, and moreover, the vibration response isn’t periodic but quasi-periodic due to impacts. The self-adaptive characteristic of the steel ball is fully demonstrated by assuming that the ball impacts the beam on a virtual location instead of the actual location observed in experiments. In general, all numerical results agree with those of experiments.

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18

Ye, Huawen, Weihua Gui, and Chunhua Yang. "Novel Stabilization Designs for the Ball-and-Beam System." IFAC Proceedings Volumes 44, no. 1 (January 2011): 8468–72. http://dx.doi.org/10.3182/20110828-6-it-1002.01725.

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19

Guinaldo, M., H. Vargas, J. Sánchez, E. Sanz, and S. Dormido. "Web-based Control Laboratory: The Ball and Beam System." IFAC Proceedings Volumes 42, no. 24 (2010): 174–79. http://dx.doi.org/10.3182/20091021-3-jp-2009.00033.

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20

Garai, Somnath, and N. H. Balasubramanyam. "Tuning of Ball and Beam System using Cascade Control." International Journal of Engineering and Management Research 10, no. 4 (August 8, 2020): 56–59. http://dx.doi.org/10.31033/ijemr.10.4.9.

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21

Andreev, F., D. Auckly, L. Kapitanski, A. Kelka, and W. White. "Matching Control Laws for a Ball and Beam System." IFAC Proceedings Volumes 33, no. 2 (March 2000): 157–58. http://dx.doi.org/10.1016/s1474-6670(17)35563-5.

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22

Burakov, Mikhail. "Fuzzy-modal control for the ball and beam system." MATEC Web of Conferences 113 (2017): 01006. http://dx.doi.org/10.1051/matecconf/201711301006.

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23

Choudhary, M. K., and G. Naresh Kumar. "ESO Based LQR Controller for Ball and Beam System." IFAC-PapersOnLine 49, no. 1 (2016): 607–10. http://dx.doi.org/10.1016/j.ifacol.2016.03.122.

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24

Korkmaz, Mehmet, and Omer Aydogdu. "Fractional Order Controller Design for Ball and Beam System." Applied Mechanics and Materials 313-314 (March 2013): 544–48. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.544.

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Fractional order controllers which has mostly used recently have investigated in this paper. It is benefit from ball & beam system to show effects of controllers. Fractional order controller and its integer form are compared with simulation results for the mentioned system. Parameters of controllers have obtained by using evolutionary algorithms techniques which are particle swarm optimization (PSO) and genetic algorithms (GAs). According to results, it is confirmed the advantage of fractional controllers. Beside, PSO has a little bit superiority over GAs technique for determining optimum values of controller parameters.

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25

Tsarik, Vladimir. "Oscillation control in the underactuated “Ball and Beam” system." IFAC-PapersOnLine 53, no. 2 (2020): 9227–31. http://dx.doi.org/10.1016/j.ifacol.2020.12.2204.

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26

LIN, Chin E., Cheng Chang KER, and Rong Tyai WANG. "Magnetic Suspension Actuator Control in Ball and Beam System Demonstration." JSME International Journal Series C 49, no. 4 (2006): 1018–26. http://dx.doi.org/10.1299/jsmec.49.1018.

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27

Kharola, Ashwani, and Pravin Patil. "Ball and beam system control using PID-based ANFIS controllers." International Journal of Advanced Mechatronic Systems 7, no. 1 (2016): 24. http://dx.doi.org/10.1504/ijamechs.2016.079649.

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28

Hammadih, Mohammad Luai, Khalifa Al Hosani, and Igor Boiko. "Interpolating sliding mode observer for a ball and beam system." International Journal of Control 89, no. 9 (March 23, 2016): 1879–89. http://dx.doi.org/10.1080/00207179.2016.1161235.

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29

Herrera Granda, Israel David. "Controller comparison and mathematical modelling of ball and beam system." INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES 2, no. 1 (June 4, 2020): 18. http://dx.doi.org/10.53358/ideas.v2i1.349.

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El presente proyecto muestra una comparación entre tres técnicas de control aplicadas en el sistema de bola y barra, en el cual una barra es girada usando un motor y esta rotación hace que una esfera ruede sobre la barra hasta alcanzar la posición deseada. El modelado matemático se realizó escribiendo las ecuaciones dinámicas en el espacio de estado, y las ecuaciones de estado no lineal se representaron como un sistema de planta utilizando Simulink del software Matlab. Las técnicas de control aplicadas fueron el controlador de retroalimentación de estado, el regulador cuadrático lineal (LQR) y el controlador NARMA basado en redes neuronales. Las tres técnicas se aplicaron para señales continuas y discretas y se probaron con y sin un observador de estado. Se realizaron varias simulaciones utilizando Matlab y Symulink, y los resultados mostraron que el sistema puede ser estabilizado por todos los controladores con ligeras diferencias, la retroalimentación de estado fue más rápida y el modelo NARMA fue más agil y requirió una entrada más pequeña.

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30

Ali, Shahad Sami. "Position control of ball and beam system using robust h∞ loop shaping controller." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 91. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp91-98.

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<p>Laboratory Ball and Beam prototype (B&B) is a system designed to implement the controlling of space application studies such as aircraft flight and land. In this paper, to control the position of the rolling ball on the beam, MATLAB program will be used to design and implement PID and robust H<sub>∞ </sub>Loop Shaping controllers. The open loop response of the system is unstable, because the ball continuously rolling on the beam when a constant input applied. To stabilize the system, a PID controller used first to achieve the desired position. Then, robust H<sub>∞ </sub>Loop Shaping controller was used to achieve performance requirement for system with uncertainties. Results for the step response shows that robust H<sub>∞ </sub>Loop Shaping controller response have no over shoot, faster about 80 times when compared to step response of PID controller, it's more effective and had better performance compared to other controllers in the control of B&B system.</p>

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31

Kara, Tolgay, and İlyas Eker. "Adaptive approximate input–output linearizing control with applications to ball and beam mechanism." Transactions of the Institute of Measurement and Control 40, no. 4 (December 5, 2016): 1201–11. http://dx.doi.org/10.1177/0142331216680150.

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This paper presents the design and implementation of adaptive control with approximate input–output linearization for underactuated open-loop unstable non-linear mechanical systems. Control of a ball and beam (BB) mechanism is selected as a benchmark problem for testing the designed control. The method of input–output linearization is reviewed and an adaptive input–output linearizing control design procedure is given. An approximate BB model is developed using Euler–Lagrange equations, and input–output linearization-based adaptive tracking control is designed for the system. The model is parameterized with respect to ball mass for adaptive tracking, and the proposed control structure is tested via computer simulations and experiments. The results present the tracking performance of designed control for various ball masses, and reveal the proposed method’s capability to cover ball mass variations over non-adaptive control. The proposed control exhibits improved error performance in the presence of parametric variations in the plant. Results of the BB control case reveal successful control of underactuated non-linear mechanisms when a system parameter is unknown or time varying.

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32

Salazar, C. S., and E. M. Bonilla. "Linearizing the Ball and Beam System with a PD Control Law." IFAC Proceedings Volumes 28, no. 14 (June 1995): 275–80. http://dx.doi.org/10.1016/s1474-6670(17)46843-1.

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33

Aziz, N. N. Abdul, M. I. Yusoff, M. I. Solihin, and R. Akmeliawati. "Two degrees of freedom control of a ball and beam system." IOP Conference Series: Materials Science and Engineering 53 (December 20, 2013): 012070. http://dx.doi.org/10.1088/1757-899x/53/1/012070.

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34

Li, En, Zi-Ze Liang, Zeng-Guang Hou, and Min Tan. "Energy-based balance control approach to the ball and beam system." International Journal of Control 82, no. 6 (May 8, 2009): 981–92. http://dx.doi.org/10.1080/00207170802061269.

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35

Taifour Ali, A., Ahmed A. M., Almahdi H. A., Osama A. Taha, and A. Naseraldeen A. "Design and Implementation of Ball and Beam System Using PID Controller." Automatic Control and Information Sciences 3, no. 1 (August 26, 2017): 1–4. http://dx.doi.org/10.12691/acis-3-1-1.

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36

Lee, Kyung-Tae, Min-Gil Jeong, and Ho-Lim Choi. "Control of a Ball and Beam System using Switching Control Method." Transactions of The Korean Institute of Electrical Engineers 66, no. 1 (January 1, 2017): 72–81. http://dx.doi.org/10.5370/kiee.2017.66.1.72.

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37

Rahmat, Mohd Fuaad, Herman Wahid, and Norhaliza Abdul Wahab. "APPLICATION OF INTELLIGENT CONTROLLER IN A BALL AND BEAM CONTROL SYSTEM." International Journal on Smart Sensing and Intelligent Systems 3, no. 1 (2010): 45–60. http://dx.doi.org/10.21307/ijssis-2017-378.

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38

Jo, N. H., and J. H. Seo. "A state observer for nonlinear systems and its application to ball and beam system." IEEE Transactions on Automatic Control 45, no. 5 (May 2000): 968–73. http://dx.doi.org/10.1109/9.855562.

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39

Danilo Montoya, Oscar, Walter Gil-González, and Carlos Ramírez-Vanegas. "Discrete-Inverse Optimal Control Applied to the Ball and Beam Dynamical System: A Passivity-Based Control Approach." Symmetry 12, no. 8 (August 14, 2020): 1359. http://dx.doi.org/10.3390/sym12081359.

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This express brief deals with the problem of the state variables regulation in the ball and beam system by applying the discrete-inverse optimal control approach. The ball and beam system model is defined by a set of four-order nonlinear differential equations that are discretized using the forward difference method. The main advantages of using the discrete-inverse optimal control to regulate state variables in dynamic systems are (i) the control input is an optimal signal as it guarantees the minimum of the Hamiltonian function, (ii) the control signal makes the dynamical system passive, and (iii) the control input ensures asymptotic stability in the sense of Lyapunov. Numerical simulations in the MATLAB environment allow demonstrating the effectiveness and robustness of the studied control design for state variables regulation with a wide gamma of dynamic behaviors as a function of the assigned control gains.

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40

Tar, József K., Imre J. Rudas, and Béla Pátkai. "Comparison of Fractional Robust- and Fixed Point Transformations- Based Adaptive Compensation of Dynamic Friction." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (November 20, 2007): 1062–71. http://dx.doi.org/10.20965/jaciii.2007.p1062.

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The features of fractional order robust and fixed-point transformation based adaptive controllers of a “Ball-Beam System” are compared to each other. The speciality of this task is that the position of the ball along the beam is indirectly controlled via directly controlling the other axis, the tilting angle of the beam. It is assumed that this tilting axle suffers from considerable dynamic friction mathematically approximated by the LuGre model. By neglecting the internal physics of the tilting drive this system can be modeled as a 4thorder one because only the 4thtime-derivative of the ball’s position can directly be influenced by the tilting torque. The system also has saturation since the available acceleration of the ball is limited by the gravitation. It is shown that little reduction of the order of the differential equation controlling the decay of the error metrics in a Sliding Mode / Variable Structure controller considerably improves the robust controller. However, really precise solution can be obtained by the adaptive controller. These statements are illustrated and substantiated via simulation.

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41

Bo, Liu, and Lin Tang. "The Dynamic Swing-Up Control of Ball and Beam System Based on Periodic Input." Applied Mechanics and Materials 635-637 (September 2014): 1275–80. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1275.

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This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. In this paper, the motion of the ball and beam system based on periodic input is studied, the chaos of the system is discussed , and the relationship of the chaos and the input parameters is analyzed . on the premise of avoiding chaos, the trajectory motion law of points with suitable oscillation parameters is studied in the poincare cross-sectional view. And the swing-up control law of ball and beam system is submitted, it consists of such three parts: increasing the range of swing , adjustment and maintain constant swing .Finally, the simulations result is given to verify the efficiency of control law.

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42

Andreev, F., D. Auckly, S. Gosavi, L. Kapitanski, A. Kelkar, and W. White. "Matching, linear systems, and the ball and beam." Automatica 38, no. 12 (December 2002): 2147–52. http://dx.doi.org/10.1016/s0005-1098(02)00145-0.

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43

Wang, Ze Fang, and Miao Song. "Research on Modeling Method Based Kernel Principal Component Analysis for Ball and Beam System." Applied Mechanics and Materials 233 (November 2012): 292–96. http://dx.doi.org/10.4028/www.scientific.net/amm.233.292.

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To simulate the human control behavior, and to solve the dimension disaster problem of the simulation model which can simulate complicated controlling behavior, the ball and beam system is researched. The kernel principal component analysis and false nearest neighbor method is adopted, and the simplified ball and beam system controller is designed. The embedding dimension of input time series is determined by the false nearest neighbor method, and then the characteristic value is extracted by the kernel principal component analysis method, so the nonlinear auxiliary variable space feature can be extracted, phase space is reconstructed and the variable is selected. Last from simplified input space to the output space regression mathematical model is fit by using the method of least squares linear regression. Test shows that the control algorithm is effective, with high control precision and stability.

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44

Kim, Hyun Do, Kyung Hyun Oh, and Ho Lim Choi. "Output Feedback Control of a Ball and Beam System Based on Jacobian Linearization under Sensor Noise." Advanced Engineering Forum 2-3 (December 2011): 85–90. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.85.

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In this paper, we consider a control problem of a ball and beam system with sensor noise on feedback sensor. If sensor noise exists in sensor's signal, it can make feedback signals deformed and then it can lead to control performance degradation or even system failure. Therefore, we need to design a robust controller to deal with the possible sensor noise in the feedback information. We develop an output feedback controller with a gain-scaling factor in order to minimize the effect of AC sensor noise in output feedback information. Our proposed controller is applied to a ball and beam system and verified by analysis and simulation. As a result, our controller reduces the effect of sensor noise to arbitrarily small by increasing a gain-scaling factor.

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Shih, Ching-Long, Jung-Hsien Hsu, and Chi-Jen Chang. "Visual Feedback Balance Control of a Robot Manipulator and Ball-Beam System." Journal of Computer and Communications 05, no. 09 (2017): 8–18. http://dx.doi.org/10.4236/jcc.2017.59002.

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Verrelst, H., K. Van Acker, J. Suykens, B. Motmans, B. De Moor, and J. Vandewalle. "Application of NLq Neural Control Theory to a Ball and Beam System." European Journal of Control 4, no. 2 (January 1998): 148–57. http://dx.doi.org/10.1016/s0947-3580(98)70109-8.

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Takahashi, Nobuya, Osamu Sato, and Masahiro Yokomichi. "Discrete-time robust controller and observer for a ball and beam system." Artificial Life and Robotics 24, no. 2 (November 21, 2018): 239–44. http://dx.doi.org/10.1007/s10015-018-0506-2.

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Ubarnes, Fernando Regino, Edwin Espinel Blanco, and Angie Ruedas Rodriguez. "Generalized proportional integral control (GPI) design for a ball and beam system." Contemporary Engineering Sciences 11, no. 90 (2018): 4447–54. http://dx.doi.org/10.12988/ces.2018.89485.

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LIN, Chin E., Wei Cheng HUANG, and Yen Chu CHANG. "Hybrid Mode Control in Improvement to Magnetic Suspension Ball and Beam System." Journal of System Design and Dynamics 4, no. 5 (2010): 738–53. http://dx.doi.org/10.1299/jsdd.4.738.

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Almutairi, Naif B., and Mohamed Zribi. "On the sliding mode control of a Ball on a Beam system." Nonlinear Dynamics 59, no. 1-2 (June 3, 2009): 221–38. http://dx.doi.org/10.1007/s11071-009-9534-8.

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