Abstract
This study is about a development and investigation of the neural network controller of the stabilization system of a moving object on a plane with its hardware implementation on the FPGA. It consists of a designing balloon balancing model, hardware and software for this layout. The platform ball balancing system consists of a black plastic plate with a white table tennis ball, a drive mechanism for tilting a plate around two axes, a digital video camera, tracking the position of the ball, hardware and software that processes the information and manages the system in real time mode. The physical modeling of the system is carried out and the equation of motion of the ball in the plane is deduced. The equations of motion are nonlinear and unsuitable for the synthesis of control systems based on linear control theory. A generalized block diagram of a neurocontroller based on the FPGA, which implements the basic components of neural network control systems, is developed. To control the position of the ball on the platform a neural network control system with feedback was developed. The scheme is a classical scheme of specialized inverse training.
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References
Kravets, P.I., Shymkovych, V.M., Samotyy, V.: Method and technology of synthesis of neural network models of object control with their hardware implementation on FPGA. Paper Presented at the Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, vol. 2, pp. 947–951 (2017). https://doi.org/10.1109/idaacs.2017.8095226
Samotyy, V., Telenyk, S., Kravets, P., Shymkovych, V., Posvistak, T.: A real time control system for balancing a ball on a platform with FPGA parallel implementation. Tech. Trans. 5, 109–118 (2018)
Kravets, P.I., Lukina, T.I., Zherebko, V.A., Shimkovich, V.N.: Methods of hardware and software realization of adaptive neural network PID controller on FPGA-Chip. J. Autom. Inf. Sci. 43(4), 70–77 (2011)
Doroshenko, A., Shymkovych, V., Fedorenko, V.: Software means of modeling of the vector type of reactive engine control system. In: CEUR Workshop Proceedings 2139, pp. 296–305 (2018)
Artem, V., Volodymyr, S., Ivan, V., Vladyslav, V.: Research and development of a stereo encoder of a FM-transmitter based on FPGA. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol. 754. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_10
Kravets, P.I., Shymkovych, V.M., Zubenko, G.A.: Technology of hardware and software implementation of artificial neurons and artificial neural networks by means of FPGA. Visnyk NTUU “KPI” Informatics, Operation and Computer Science, № 55, pp. 174–180 (2012)
Mukhin, V., Volokyta, A., Heriatovych, Y., Rehida, P.: Method for efficiency increasing of distributed classification of the images based on the proactive parallel computing approach. Adv. Electr. Comput. Eng. 18(2), 117–122 (2018). https://doi.org/10.4316/AECE.2018.02015
Hu, Z., Mukhin, V., Kornaga, Y., Volokyta, A., Herasymenko, O.: The scheduler for distributed computer systems based on the network centric approach to resources control. Paper Presented at the Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, vol. 1, pp. 518–523 (2017). https://doi.org/10.1109/idaacs.2017.8095135
Chu, P.P.: RTL Hardware Design Using VHDL. Coding for Efficiency, Portability, and Scalability, p. 669. Wiley, Hoboken (2006)
Mohammed, R.H., Elnaghi, B.E., Bendary, F.A., Elserfi, K.: Trajectory tracking control and robustness analysis of a robotic manipulator using advanced control techniques. Int. J. Eng. Manuf. (IJEM) 8(6), 42–54 (2018). https://doi.org/10.5815/ijem.2018.06.04
Karam, E., Mjeed, N.: Modified integral sliding mode controller design based neural network and optimization algorithms for two wheeled self balancing robot. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 10(8), 11–21 (2018). https://doi.org/10.5815/ijmecs.2018.08.02
Teslyuk, V., Beregovskyi, V., Denysyuk, P., Teslyuk, T., Lozynskyi, A.: Development and implementation of the technical accident prevention subsystem for the smart home system. Int. J. Intell. Syst. Appl. (IJISA) 10(1), 1–8 (2018). https://doi.org/10.5815/ijisa.2018.01.01
Geraldine Bessie Amali, D., Dinakaran, M.: A new quantum tunneling particle swarm optimization algorithm for training feedforward neural networks. Int. J. Intell. Syst. Appl. (IJISA) 10(11), 64–75 (2018). https://doi.org/10.5815/ijisa.2018.11.07
Sharma, A., Kulshrestha, S., Daniel, S.B.: Machine learning approaches for cancer detection. Int. J. Eng. Manuf. (IJEM) 8(2), 45–55 (2018). https://doi.org/10.5815/ijem.2018.02.05
Mirzadeh, M., Haghighi, M., Khezri, S., Mahmoodi, J., Karbasi, H.: Design adaptive fuzzy inference controller for robot arm. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 6(9), 66–73 (2014). https://doi.org/10.5815/ijitcs.2014.09.09
Kravets, P.I., Shimkovich, V.N., Ferens, D.A.: Method and algorithms of implementation on PLIS the activation function for artificial neuron chains. Elektronnoe Modelirovanie 37(4), 63–74 (2015)
Kravets, P.I., Shimkovich, V.N., Omelchenko, P.: Neural network components of the systems of control of dynamic objects and their hardware-software implementation on FPGA. Visnyk NTUU “KPI” Informatics, operation and computer systems, № 59, pp. 78–85 (2013)
Kravets, P.I., Shimkovich, V.N.: Method of optimization of weight coefficients of neuron networks by means of genetic algorithm under implementation on programmed logical integral circuits. Elektronnoe Modelirovanie 35(3), 65–75 (2013)
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Kravets, P., Shymkovych, V. (2020). Hardware Implementation Neural Network Controller on FPGA for Stability Ball on the Platform. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_23
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