Control issues, artificial neural network (ANN) for acrobot system

Author:

Danh Nguyen

Abstract

Acrobot is a robotic system with several levels of operational states investigated by the author. Due to the limited nature of the investigation under certain ideal conditions, designers have to create some algorithms that control the system most appropriately in a given working environment. In this paper, the author proposed the problem of designing, modeling and controlling an acrobot system, including ANN. Mathematical models, Simulink are also presented in a specific way. Simulation parameters have been adjusted to be the most suitable and intuitive. Based on the simulation data, the performance analysis of the system becomes more accurate. Above suggestions are intended to serve vocational education and scientific research. ANN is the most intelligent control method currently added in this paper to firmly confirm its effectiveness in all problems. Proposing control strategies for different models is also applied by the author. 

Publisher

European Alliance for Innovation n.o.

Subject

General Medicine

Reference20 articles.

1. Gopi Krishna Rao P. V., Subramanyam M. V., Satyaprasad K.: “Performance Comparison of PID Controller Tuned using Classical and Genetic Algorithm Methods”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 6, Number 14, pp. 1757-1766, (2011).

2. Papoutsidakis M., Piromalis D., Neri F., Camilleri M.: “Intelligent Algorithms Based on Data Processing for Modular Robotic Vehicles Control”, WSEAS Transactions on Systems, volume 13, 2014.

3. Nguyen H. T., Nguyen M. T., Nguyen V. D. H., Doan T. T., Vo C. P.: “Designing PID-Fuzzy Controller for Pendubot System”, Robotica & Management, Vol. 22 No. 2, pp. 8-12, December, 2017.

4. Ramm A., Sjöstedt M.: “Reaction Wheel Balanced Robot, Design And Sensor Analysis Of Inverted Pendulum Robot”, Stockholm, Sweden 2015.

5. Zhang A., She J., Lai X., Wu M.: “Motion Planning and Tracking Control for an Acrobat Base on a Rewinding Approach”, Automatica 49, 278-284, 2013.

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